Health Assessment via Eye Movement Biometrics

ABSTRACT

Methods and systems for assessing a health state of a person via eye movement-driven biometric systems are provided. Examples of the health states that it would be possible to detect with such a system are but not limited to brain injuries (e.g., concussions), dementia, Parkinson&#39;s disease, post-traumatic stress syndrome, schizophrenia, fatigue, cybersickness, autism, Bipolar Disorder and other health conditions that manifest themselves in abnormal behavior of the human visual system. Described methods and systems can also detect influence of alcohol and/or drugs. The system extracts biometric template of a person by deriving features from the captured eye movement signal. The system may compare the difference between previous healthy state of a tested person and newly captured template or an averaged biometric template created from the records of multiple healthy people state of multiple people and a newly captured template from a person who needs to be tested. Based on the difference between the templates, a decision of a health state of a person is made. Described methods and systems may work on any device that has eye tracking capabilities including but not limited to desktop mounted eye tracking systems, head mounted eye tracking systems such as Virtual Reality and Augmented Reality or stand-alone mounted eye tracking systems.

PRIORITY CLAIM

This application is a continuation-in-part of International Application No. PCT/US2015/027625 Entitled: “DETECTION OF BRAIN INJURY AND SUBJECT STATE WITH EYE MOVEMENT BIOMETRICS” filed on Apr. 24, 2015, which claims priority to U.S. Provisional Patent Application No. 61/984,518 Entitled: “DETECTION OF BRAIN INJURY WITH EYE MOVEMENT BIOMETRICS” filed on Apr. 25, 2014 and U.S. Provisional Patent Application No. 62/017,138 filed Jun. 25, 2014 Entitled: “AUTISM AND FATIGUE DETECTION USING EYE MOVEMENT BEHAVIOR” filed on Jun. 25, 2014, the disclosures of all three of which are incorporated herein by reference in their entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under CNS-1250718 awarded by the National Science Foundation, and 60NANB10D213 and #60NANB12D234 awarded by the National Institute of Standards. The government has certain rights in the invention.

BACKGROUND

Field

This disclosure is generally related to person identification, verification, and subject state detection, and more specifically to methods and systems for detecting brain injury using ocular biometric information.

Description of the Related Art

Accurate, non-intrusive, and fraud-resistant identity recognition is an area of increasing concern in today's networked world, with the need for security set against the goal of easy access. Many commonly used methods for identity determination have known problems. For example, password verification has demonstrated many weaknesses in areas of accuracy (the individual typing the password may not actually be its owner), usability (people forget passwords), and security (people write passwords down or create easy-to-hack passwords).

The communication between a human and a computer frequently begins with an authentication request. During this initial phase of interaction a user supplies a system with verification of his/her identity, frequently given in the form of a typed password, graphically encoded security phrase, or a biometric token such as an iris scan or fingerprint. In cases when the user is prompted to select the identification key from a sequence of numerical and graphical symbols, there is a danger of accidental or intentional shoulder surfing performed directly or by use of a hidden camera. Moreover, such challenges may become specifically pronounced in cases of multi-user environments including shared-workstation use and more contemporary interaction media such as tabletops. Authentication methods requiring remembrance of information such as symbols and photos have reduced usability, due to the fact that long, sophisticated passwords can be easily forgotten and short passwords are easy to break. Even biometric methods such as iris and finger print-based authentication may not be completely fraud-proof, since they are based on a human's body characteristics that can be replicated.

There are a number of methods employed today for biometric purposes. Some examples include the use of fingerprints, iris, retina scans, face recognition, hand/finger geometry, brain waves, periocular features, ear shape, gait, and voice recognition. Iris-based identification is considered to be one of the most accurate among existing biometric modalities. However, commercial iris-identification systems may be easy to spoof, and they are also inconvenient and intrusive since they usually require a user to stand very still and very close to the image capturing device.

The human eye includes several anatomical components that make up the oculomotor plant (OP). These components include the eye globe and its surrounding tissues, ligaments, six extraocular muscles (EOMs) each containing thin and thick filaments, tendon-like components, various tissues and liquids.

The brain sends a neuronal control signal to three pairs of extraocular muscles, enabling the visual system to collect information from the visual surround. As a result of this signal, the eye rotates in its socket, exhibiting eye movement such as the following types: fixation, saccade, smooth pursuit, optokinetic reflex, vestibulo-ocular reflex, and vergence. In a simplified scenario, when a stationary person views a two-dimensional display (e.g., computer screen), three eye movement types are exhibited: fixations (maintaining the eye directed on the stationary object of interest), saccades (rapid eye rotations between points of fixation with velocities reaching 700°/s), and smooth pursuit (movements that occur when eyes are tracking a smooth moving object).

Accurate estimation of oculomotor plant characteristics is challenging due to the secluded nature of the corresponding anatomical components, which relies on indirect estimation and includes noise and inaccuracies associated with the eye tracking equipment, and also relies on effective classification and filtering of the eye movement signal.

According to reports from the Centers for Disease Control and Prevention, approximately 1.7 million people are diagnosed with traumatic brain injury (“TBI”) each year in the United States. Of these, nearly 75% (or 1.3 million) are incidences of mild traumatic brain injury (“mTBI”). This does not account for the undiagnosed occurrences of mTBI.

Each year there are approximately 52,000 TBI-related deaths in the United States, accounting for roughly one-third (30.5%) of all injury-related deaths. mTBI increases the risk of TBI, and can cause neurological disorders which persist years after injury, affecting thought, behavior, and emotion, producing physical symptoms such as fatigue, nausea, vertigo, headache, lethargy, and blurred vision. The ability to diagnose mTBI is especially important for active military personnel and professional sports players, for whom it is common to sustain repeated head trauma, the severity of which can range from inconsequential to severe. Unfortunately, there are few quantitative measures by which to assess the presence and severity of TBI, with health care professionals often employing qualitative guidelines to assist in diagnosis.

User fatigue detection is an important problem in the modern world. Digital-media use expected to grow to an average of 15.5 hours day, resulting in excessive levels of fatigue. Moreover, objective fatigue detection is of great significance for HCI research to ensure scientific soundness of experimentation involving learning, attention deployment, and boredom studies. Fatigue detection is also important in everyday life activities such as driving. According to the National Sleep Foundation, National Highway Traffic Safety Administration reports indicate at least 100,000 police-reported crashes each year are the direct result of fatigue-related “drowsy driving”, resulting in 1,550 deaths and 71,000 injuries.

Autism detection is an important problem in the modern world, especially early autism detection in children. Centers for Disease Control and Prevention (CDC) report that 1 in 68 children is identified with autism spectrum disorder. The number is 30% higher than in 2008 and 120% higher than in 2002. The exact causes for autism are unknown. Most children who are autistic are not diagnosed until after age 4, even though children can be diagnosed as early as age 2. Early detection of autism is very important because early correction therapies can be applied to help a child to develop normally.

Human eye movements, when a person is sitting in front of a computer screen and executing an HCI-related task, consist of three eye movements: fixations, saccades, and smooth pursuit. Fixations are executed when a user is looking at a stationary target. During fixations high acuity visual information captured by the eye is sent to the brain. Saccades are rapid, stereotyped, ballistic eye movements with velocities reaching up to 700°/s. Human visual system is effectively blind during saccades. Smooth pursuits are executed in response to a smooth moving target with various quality of vision maintained depending how well the target is tracked.

SUMMARY

Methods and systems for detecting brain injury using ocular biometric information are described. In an embodiment, a method of assessing brain injuries includes measuring eye movement of a person. One or more values (e.g., a feature vector) are determined based on the measured eye movements. Based on the determined values, an assessment is made of whether or not the person has suffered brain injury. In some embodiments, the method includes assessing whether the person has suffered mild traumatic brain injury (mTBI) based the measured eye movements.

In an embodiment, system includes a processor and a memory coupled to the processor and configured to store program instructions executable by the processor to implement a method of assessing brain injuries includes measuring eye movement of a person. One or more values are determined based on the measured eye movements. Based on the determined values, an assessment is made of whether or not the person has suffered brain injury.

In an embodiment, a tangible, computer readable medium includes program instructions are computer-executable to implement a method of assessing brain injuries includes measuring eye movement of a person. One or more values are determined based on the measured eye movements. Based on the determined values, an assessment is made of whether or not the person has suffered brain injury.

Methods and systems for detecting autism and/or fatigue using ocular biometric information are also described. In an embodiment, a method of detecting autism or/and fatigue in a person includes measuring eye movement of a person. One or more values are determined based on the measured eye movement. Based on the determined one or more values, autism or/and fatigue in the person is detected. In some embodiments, autism or/and fatigue is detected based on one or more behavioral scores determined from the measured eye movement.

In an embodiment, system includes a processor and a memory coupled to the processor and configured to store program instructions executable by the processor to implement a method of detecting autism and/or fatigue in a person includes measuring eye movement of a person. One or more values are determined based on the measured eye movement. Based on the determined one or more values, autism and/or fatigue in the person is detected.

In an embodiment, a tangible, computer readable medium includes program instructions are computer-executable to implement a method of detecting autism or/and fatigue in a person includes measuring eye movement of a person. One or more values are determined based on the measured eye movement. Based on the determined one or more values, autism or/and fatigue in the person is detected.

In an embodiment, a method of making a biometric assessment includes measuring eye movement of a subject and assessing values for one or more characteristics from the measured eye movement. A physical condition of the subject based is assessed based on the values assessed for at least one of the characteristics. In some embodiments, biometric assessment includes autism and/or fatigue assessment based on behavioral scores.

In various embodiments, automated detection methods for autism and/or fatigue include eye movement analysis. Eye movements are the result of the activity of the several brain zones and are executed by the oculomotor plant that consists of extraocular muscles and the eye globe. In some embodiments, eye movements signal autism. In some embodiments, eye movements signal mental fatigue, physical fatigue, or both. Accordingly, a comprehensive assessment framework is available from a single data-capturing device such as an eye tracker. Eye tracking and subsequent eye movement analysis may be used to satisfy following aspects of detection for assessing user fatigue: high sensitivity, real-time assessment capability, user friendliness, and noninvasiveness. Eye tracking and subsequent eye movement analysis may be used to satisfy autism detection.

In some embodiments, fatigue onset is signaled by increased corrective eye movement behavior, which behavior scores are able to detect. In some embodiments, correlations are made between self-reported fatigue assessment and objective metrics described herein and objective detection of corrective behavior with a set of standardized metrics.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates one embodiment of assessing a person's identity using multimodal ocular biometrics based on eye movement tracking and measurement of external characteristics.

FIG. 2 illustrates one embodiment of authentication using oculomotor plant characteristics, complex eye movement patterns, iris and periocular information.

FIG. 3 is a block diagram illustrating architecture for biometric authentication via oculomotor plant characteristics according to one embodiment.

FIG. 4 illustrates raw eye movement signal with classified fixation and saccades and an associated oculomotor plant characteristics biometric template.

FIG. 5 is a graph illustrating receiver operating curves for ocular biometric methods in one experiment.

FIG. 6 illustrates one embodiment of a system for ocular biometric assessment of a user.

FIG. 7 illustrates one embodiment of a system for biometric assessment of a user wearing an eye-tracking headgear system.

FIG. 8 is a set of graphs illustrating examples of complex oculomotor behavior.

FIG. 9 illustrates a spoof attack via pre-recorded signal from the authentic user.

FIG. 10 illustrates eye movement for an authentic, live user.

FIG. 11 illustrates an example of the difference between “normal” and “coercion” logins.

FIG. 12 illustrates a second example of the difference between “normal” and “coercion” logins.

FIG. 13 illustrates biometric assessment with subject state detection and assessment.

FIG. 14 illustrates a comparative distribution of fixation over multiple recording sessions.

FIGS. 15A and 15B are graphs of a receiver operating characteristic in which true positive rate is plotted against false acceptance rate for several fusion methods.

FIGS. 16A and 16B are graphs of a cumulative match characteristic for several fusion methods.

FIG. 17 illustrates one embodiment of assessing brain injury based on measurements of eye movement of a person.

FIG. 18 is a histogram presenting mTBI detection scores for mTBI determined using a supervised technique.

FIG. 19 is a histogram presenting mTBI detection scores for mTBI determined using an unsupervised technique.

FIG. 20 is a confusion matrix for biometric assessment of mTBI from a supervised technique.

FIG. 21 is a confusion matrix for biometric assessment of mTBI from an unsupervised technique.

FIG. 22 illustrates one embodiment of detecting autism or/and fatigue.

FIG. 23 is a bar graph representing fixation quantitative scores for the sessions.

FIG. 24 shows a regression for fixation qualitative scores for a session.

FIG. 25 is a bar graph representing saccade quantitative scores for sessions measuring eye movement of users.

FIG. 26 shows a regression for average fixation duration for a session.

FIG. 27 is a bar graph representing average number of saccades.

FIG. 28 is a bar graph representing average saccade amplitude.

FIG. 29 is a bar graph representing average saccade duration.

FIG. 30 is a bar graph showing average saccade peak velocity.

FIG. 31 includes some examples of fixation drifts and their basic characteristics.

FIG. 32 shows examples of fixation velocity and acceleration profiles.

FIG. 33 shows examples indicating the shape (amplitude and curvature) of saccades in the time domain and in the 2D-plane.

FIG. 34 shows examples of saccade velocity and acceleration profiles and their basic characteristics.

FIG. 35 shows examples of saccade main sequence characteristics.

FIG. 36 shows examples of glissades and their basic characteristics.

FIG. 37 shows examples of the horizontal and vertical components of the acceleration profile of a glissade.

FIG. 38 shows eye movement signal and the corresponding pupil variation during a reading-pass of text stimulus.

FIG. 39 shows results from application of the technology from subjects with mTBI (mild traumatic brain injury).

While the invention is described herein by way of example for several embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments or drawings described. It should be understood, that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including, but not limited to.

DETAILED DESCRIPTION OF EMBODIMENTS

As used herein, traumatic brain injury (“TBI”) means any physical trauma that results in memory loss, altered mental state, loss of consciousness, or focal neurological deficits. TBI may be classified as “mild” (mTBI) when loss of consciousness does not exceed 30 minutes, Glasgow Coma Scale does not exceed 13-15 after 30 minutes, and memory loss does not extend beyond a 24-hour period.

As used herein, “fixation quantitative score” means the ratio of measured fixation points against the total number of stimuli.

As used herein, “fixation count” means the total number of measured fixations.

As used herein, “multi-corrected undershoot” means the number of saccades which undershoot the target stimulus and are followed by more than one corrective saccade.

As used herein, “fixation duration” means the average fixation duration across a recording.

As used herein, “vectorial saccade amplitude” represents the average Euclidean distance covered by each saccade.

As used herein, “simple overshoot” means the number of saccades that overshoot the target stimulus and are not followed by corrective saccades.

As used herein, “oculomotor plant” means the eye globe and its surrounding tissues, ligaments, and extraocular muscles (EOMs), each of which may contain thin and thick filaments, tendon-like components, various tissues and liquids.

As used herein, “scanpath” means a spatial path formed by a sequence of fixations and saccades. Fixations occur when the eye is held in a relatively stable position, allowing heightened visual acuity on an object of interest. Saccades may occur when the eye rotates quickly, for example, between points of fixation, with almost no visual acuity maintained during rotation. Velocities during saccades may reach as high as 700° per second.

As used herein, “brain control strategies” are defined as an ability of the brain to guide the eye to gather the information from the surrounding world. Strategies may be based on, or include, information on how and where the eye is guided. Brain control strategies can manifest themselves in the spatial and temporal (e.g. location and duration) characteristics of fixation, such characteristics of saccades as main-sequence relationship (relationship between maximum velocity exhibited during a saccade and its amplitude), amplitude duration relationship (relationship between saccade's duration and its amplitude), saccade's waveform (relationship between the time it takes to reach a peak velocity during a saccade to the total saccade duration) and other characteristics.

As used herein, “complex eye movement (CEM) patterns” are defined as eye movement patterns and characteristics that allow inferring brain's strategies or activity to control visual attention. This information might be inferred from individual and aggregated characteristics of a scanpath. In addition CEM can include, for example, the information about saccades elicited in response to different stimuli. Examples of forms in which CEM information may be manifested include: simple undershoot or overshoot (e.g. saccades that miss the target and no correction is made to put gaze location on the target), corrected undershoot/overshoot (e.g. saccades that miss the target, but the brain corrects eye position to the target's position), multi-corrected undershoot/overshoot—similar in definition to the corrected undershoot/overshoot saccade however additional series of corrective saccades is added that brings the resulting fixation position closer to the target; dynamic overshoot which is the oppositely directed post-saccadic eye movement in the form of backward jerk at the offset of a saccade; compound saccade which represented by an initial saccade that is subsequently followed by two or more oppositely directed saccades of small amplitude that move the eye-gaze back and forth from the target position; and express saccade which is represented by a sequence of saccades directed toward the target where the end of the initial saccade is in the small spatial and temporal proximity from the sequence of new saccades leading to the target.

As used herein, “assessing a person's identity” includes determining that a person being assessed or measured is a particular person or within a set or classification or persons. “Assessing a person's identity” also includes determining that a person being assessed is not a particular person or within a set or classification or persons (for example, scanning eye movements of Person X to determine whether or not Person X is on a list a persons authorized to access to a computer system).

In some embodiments, a person's identity is assessed using one or more characteristics that exist only in a live individual. The assessment may be used, for example, to authenticate the person for access to a system or facility. In certain embodiments, authentication of a person does not require the person being authenticated to remember any information (for example, to remember a password).

In some embodiments, a person's identity is assessed using measurements of one or more visible characteristics of the person in combination with estimates of one or more non-visible characteristics of the person. The assessment may be used to authenticate the person for access a computer system, for example.

In some embodiments, a method of assessing a person's identity includes making estimates based on eye movements of a person and measuring iris characteristics or periocular information of the person. In some embodiments, a method of assessing a characteristics of a person (such as identity, mental state, or physical state) includes making estimates based on eye movements of a person and measuring iris characteristics or periocular information of the person. Eye movements may be used to estimate oculomotor plant characteristics, brain control strategies in a form of complex eye movement patters and scanpaths, or all these characteristics. Eye movements may be used to estimate oculomotor plant characteristics, brain control strategies in a form of complex eye movement patters and scanpaths, or all these characteristics. FIG. 1 illustrates one embodiment of assessing a person's identity using multimodal ocular biometrics based on eye movement tracking and measurement of external characteristics. At 100, eye movements of a person are tracked. Eye movement data may be collected using, for example, an eye tracking instrument.

At 102, acquired eye movement data may be used to estimate oculomotor plant characteristics. Dynamic and static characteristics of the oculomotor plant that may be estimated include the eye globe's inertia, dependency of an individual muscle's force on its length and velocity of contraction, resistive properties of the eye globe, muscles and ligaments, characteristics of the neuronal control signal sent by the brain to the EOMs, and the speed of propagation of this signal. Individual properties of the EOMs may vary depending on their roles. For example, the agonist role may be associated with the contracting muscle that pulls the eye globe in the required direction, while the antagonist role may be associated with the lengthening muscle resisting the pull.

At 104, acquired eye movement data may be used to analyze complex eye movements. The CEM may be representative of the brain's control strategies of guiding visual attention. Complex eye movement patterns may be based on, for example, on individual or aggregated scanpath data. Scanpaths may include one or more fixations and one or more saccades by a person's eye. The processed fixation and saccade groups may describe the scanpath of a recording. Individual scanpath metrics may be calculated for each recording based on the properties of its unique scanpath. Basic eye movement metrics may include: fixation count, average fixation duration, average vectorial average vertical saccade amplitude, average vectorial saccade velocity, average vectorial saccade peak velocity, and the velocity waveform indicator (Q), and a variety of saccades such as: undershot/overshoot, corrected undershoot/overshoot, multi-corrected undershoot/overshoot, dynamic, compound, and express saccades. More complex metrics, resulting from the aggregated scanpath data, may include: scanpath length, scanpath area, regions of interest, inflection count, and slope coefficients of the amplitude-duration and main sequence relationships.

At 106, measurements may be taken of external characteristics of the person. In one embodiment, one or more characteristics of the person's iris or/and periocular information are measured. In certain embodiments, non-ocular external characteristics, such as a facial characteristics or fingerprints, may be acquired in addition to, or instead of external ocular characteristics. At 108, the measurements acquired at 106 are used to assess external characteristics of a person.

At 110, a biometric assessment is performed based on some or all of the estimated oculomotor plant characteristics, complex eye movement patterns, and external ocular characteristics. In some embodiments, biometric assessment is based on a combination of one or more dynamic characteristics is combined with one or more static traits, such as iris patterns or periocular information. Assessment of a person's state (including, for example, fatigue, autism, or whether the person has suffered brain injury) or identity may be carried out based on a combination of two or more of: oculomotor plant characteristics, complex eye movement patterns, external ocular characteristics. micro-eye movements, complex oculomotor behavior, or fixation density mapping.

In some embodiments, a single instrument is used to acquire all of the eye movement data and external characteristic data (for example, iris patterns or/and periocular information) for a person. In other embodiments, two or more different instruments may be used to acquire eye movement data or external characteristic data for a person.

Methods and systems as described herein may be shoulder-surfing resistant. For example, data presented during authentication procedures as described herein may not reveal any information about a user to an outside observer. In addition, methods and systems as described herein may be counterfeit-resistant in that, for example, they can be based on internal non-visible anatomical structures or complex eye movement patters representative of the brain's strategies to guide visual attention. In some embodiments, information on OPC and CEM biometric used in combination with one another to assess identity of a person.

In some embodiments, a user is authenticated by estimating individual oculomotor plant characteristics (OPC) and complex eye movement patterns generated for a specific type of stimulus. The presented visual information may be used to evoke eye movements that facilitate extraction of the OPC and CEM. The information presented can be overseen by a shoulder-surfer with no negative consequences. As a result, the authentication does not require any feedback from a user except looking at a presented sequence of images or text.

FIG. 2 illustrates one embodiment of user's authentication and/or assessment of user's state using OPC, CEM, iris, and periocular information. The OPC, CEM, iris, and periocular information may be captured by a single camera sensor. Identity assessment 200 includes use of image/light sensor 201 and eye tracking software 203. From image data captured with image/light sensor 201, eye tracking software 203 may generate raw eye positional signal data, which may be sent to the OPC and the CEM modules, and eye images, which may be sent to iris module 205 and periocular module 207. In general, all modules may process the input in the form of raw eye position signal or eye images, perform feature extraction, generate biometric templates, perform individual trait template matching 206, multi-trait template matching phase 208, and decision output 210. Feature extraction 204 includes OPC feature extraction 211, CEM/COB/FDM feature extraction 213, iris feature extraction 215, and periocular feature extraction 217. Processing of eye images includes iris module image pre-processing 231, periocular module image pre-processing 232, and iris module template generation 233. CEM/COB/FDM feature extraction 213 may involve feature extraction for one or more of CEM, COB, or FDM approaches.

At 202, eye positional signal information is acquired. Raw eye movement data produced during a recording is supplied to an eye movement classification module at 212. In some embodiments, an eye-tracker sends the recorded eye gaze trace to an eye movement classification algorithm at 212 after visual information employed for the authentication is presented to a user. An eye movement classification algorithm may extract fixations and saccades from the signal. The extracted saccades' trajectories may be supplied to the mathematical model of the oculomotor plant 214 for the purpose of simulating the exact same trajectories. At 216, an optimization algorithm modifies the values for the OPC to produce a minimum error between the recorded and the simulated signal. The values that produce the minimum error are supplied to an authentication algorithm at 218. The authentication algorithm may be driven by a Hotteling's T-square test 220. Templates may be accessible from template database 221. The Hotteling's T-square test (or some other appropriate statistical test) may either accept or reject the user from the system. An authentication probability value (which may be derived, for example, by the Hotteling's T-square test) may be propagated to decision fusion module 222. Although in the embodiment shown in FIG. 2, a Hotteling's T-square test is employed, an authentication algorithm may be driven by other suitable statistical tests. In one embodiment, an authentication algorithm uses a Student's t-test is used (which may be enhanced by voting).

Fusion module 222 may accept or reject a person, and/or making a decision about person's identity or/and about the state of the person, based on one or more similarity scores. In some case, fusion module 222 accept or reject a person based on OPC similarity score 224, CEM/COB/FDM similarity score 226, iris similarity score 270, and periocular similarity score 280. Further aspects of implementing authentication based on OPC and the other modalities are set forth below.

Eye Movement Classification: At 212, a Velocity-Threshold (I-VT) classification algorithm (or some other eye movement classification algorithm) may be employed with threshold selection accomplished via standardized behavior scores. After the classification saccades with amplitudes smaller than 0.5° (microsaccades) may be filtered out to reduce the amount of noise in the recorded data.

Oculomotor Plant Mathematical Model: At 214, a linear horizontal homeomorphic model of the oculomotor plant capable of simulating the horizontal and vertical component of eye movement during saccades may be employed. The model mathematically may represent dynamic properties of the OP via a set of linear mechanical components such as springs and damping elements. The following properties may be considered for two extraocular muscles that are modeled (medial and lateral recti) and the eye globe: active state tension—tension developed as a result of the innervations of an EOM by a neuronal control signal, length tension relationship—the relationship between the length of an EOM and the force it is capable of exerting, force velocity relationship—the relationship between the velocity of an EOM extension/contraction and the force it is capable of exerting, passive elasticity—the resisting properties of an EOM not innervated by the neuronal control signal, series elasticity—resistive properties of an EOM while the EOM is innervated by the neuronal control signal, passive elastic and viscous properties of the eye globe due to the characteristics of the surrounding tissues. The model may take as an input a neuronal control signal, which may be approximated by a pulse-step function. The OPC described above can be separated into two groups, each separately contributing to the horizontal and the vertical components of movement.

OPC Estimation Algorithm: At 230, a Nelder-Mead (NM) simplex algorithm (or some other minimization algorithm such as Trust-Region using the interior-reflective Newton method) may be used in a form that allows simultaneous estimation of all OPC vector parameters at the same time. A subset of some OPC may be empirically selected. The remaining OPC may be fixed to default values. In an example a subset of selected OPC comprises of length tension—the relationship between the length of an extraocular muscle and the force it is capable of exerting, series elasticity—resistive properties of an eye muscle while the muscle is innervated by the neuronal control signal, passive viscosity of the eye globe, force velocity relationship—the relationship between the velocity of an extraocular muscle extension/contraction and the force it is capable of exerting—in the agonist muscle, force velocity relationship in the antagonist muscle, agonist and antagonist muscles' tension intercept that ensures an equilibrium state during an eye fixation at primary eye position (for example an intercept coefficient in a linear relationship between the force that a muscle applies to the eye and the rotational position of the eye during fixation), the agonist muscle's tension slope (for example, a slope coefficient in a linear relationship between the force that an agonist muscle applies to the eye and the rotation position of the eye during fixation), the antagonist muscle's tension slope (for example, a tension slope coefficient for the antagonist muscle), and eye globe's inertia. Lower and upper boundaries may be imposed to prevent reduction or growth of each individual OPC value to less than 10% or larger than 1000% of its default value. Stability degradation of the numerical solution for differential equations describing the OPMM may be used as an additional indicator for acceptance of the suggested OPC values by the estimation algorithm. In some embodiments, a template including some or all of the OPC described above is passed to a matching module to produce a matching score between a computed template and a template already stored in the database.

Authentication and/or detection of person's state: As an input, the person authentication algorithm and/or person's state detection algorithm takes a vector of the OPC optimized for each qualifying saccade. In some embodiments, a statistical test is applied to assess all optimized OPC in the vector at the same time. In the example shown in FIG. 2, a Hotelling's T-square test is applied. The test may assess data variability in a single individual as well as across multiple individuals. In one embodiment, the Hotelling's T-square test is applied to an empirically selected subset of five estimated parameters: series elasticity, passive viscosity of the eye globe, eye globe's inertia, agonist muscle's tension slope, and the antagonist muscle's tension slope.

As a part of the authentication procedure, the following Null Hypothesis (H0) is formulated assuming datasets i and j may be compared: “H0: There is no difference between the vectors of OPC between subject i and j”. The statistical significance level (p) resulting from the Hotelling's T-square test may be compared to a predetermined threshold (for example, 0.05). In this example, if the resulting p is smaller than the threshold, the H0 is rejected indicating that the datasets in question belonged to different people. Otherwise, the H0 is accepted indicating that the datasets belonged to the same person. Two types of errors may be recorded as a result: (1) the rejection test of the H0 when the datasets belonged to the same person; and (2) the acceptance test of the H0 when the datasets were from different people.

In the method described above, variability was accounted for by applying a Hotelling's T-square test. In certain embodiments, oculomotor plant characteristics are numerically evaluated given a recorded eye-gaze trace.

Referring to the CEM side of FIG. 2, aspects of biometrics using CEM are described. In some embodiments, some aspects of biometrics using CEM in a form of scanpaths are as described in C. Holland, and O. V. Komogortsev, Biometric Identification via Eye Movement Scanpaths in Reading, In Proceedings of the IEEE International Joint Conference on Biometrics (IJCB), 2011, pp. 1-8. As noted above, raw eye movement data produced during a recording is supplied to an eye movement classification module at 212. Classified fixations and saccades forming complex eye movement patterns may be processed by two modules: individual scanpath component module 240 and aggregated scanpath module 241. Individual scanpath component module 240 may process eye movement characteristics belonging to individual fixations and saccades. Characteristics processed by the individual scanpath component module 240 may include the following:

Fixation Count—number of detected fixations. Fixation count is indicative of the number of objects processed by the subject, and was measured simply as the total number of fixations contained within the scanpath.

Average Fixation Duration—sum of duration of all fixations detected divided by fixation count. Average fixation duration is indicative of the amount of time a subject spends interpreting an object, and was measured as the sum of fixation durations over the fixation count.

Average Vectorial Saccade Amplitude—sum of vectorial saccade amplitudes over the total number of saccades, where the vectorial amplitude of a saccade was defined as the Euclidean norm of the horizontal and vertical amplitudes. There is a noted tendency for saccades to maintain similar amplitudes during reading, average saccade amplitude was considered as a candidate biometric feature under the assumption that differences in amplitude may be apparent between subjects. Average vectorial saccade amplitude was measured as the sum of vectorial saccade amplitudes over the total number of saccades, where the vectorial amplitude of a saccade was defined as the Euclidean norm of the horizontal and vertical amplitudes, according to the equation:

${{Vectorial}\mspace{14mu} {Average}} = \frac{\sum\limits_{i = 1}^{n}\; \sqrt{x_{i}^{2} + y_{i}^{2}}}{n}$

Average Horizontal Saccade Amplitude—average amplitude of the horizontal component of saccadic movement. Horizontal saccade amplitude was considered separately as these are more indicative of between-word saccades. Average horizontal saccade amplitude was measured as the sum of horizontal saccade amplitudes greater than 0.5° over the total number of horizontal saccades with amplitude greater than 0.5°.

Average Vertical Saccade Amplitude—average amplitude of the vertical component of saccadic movement. Vertical saccade amplitude was considered separately as these are more indicative of between-line saccades. Average vertical saccade amplitude was measured as the sum of vertical saccade amplitudes greater than 0.5° over the total number of vertical saccades with amplitude greater than 0.5°.

Average Vectorial Saccade Velocity—sum of vectorial saccade velocities over the total number of saccades, where the vectorial velocity of a saccade was defined as the Euclidean norm of the horizontal and vertical velocities. Average vectorial saccade velocity as measured as the sum of vectorial saccade velocities over the total number of saccades, where the vectorial velocity of a saccade was defined as the Euclidean norm of the horizontal and vertical velocities.

Average Vectorial Saccade Peak Velocity—sum of vectorial saccade peak velocities over the total number of saccades. Average vectorial saccade peak velocity was measured as the sum of vectorial saccade peak velocities over the total number of saccades, where the vectorial peak velocity of a saccade was defined as the Euclidean norm of the horizontal and vertical peak velocities.

Velocity Waveform Indicator (Q)—the relationship between the time it takes to reach a peak velocity during a saccade to the total saccade duration. We use the term velocity waveform indicator (Q) to refer to the ratio of peak velocity to average velocity of a given saccade. In normal human saccades this value is roughly constant at 1.6, though it is assumed that this is subject to some amount of variation similar to the amplitude-duration and main sequence relationships. A rough estimate of this value may be obtained from the ratio of the average vectorial peak velocity over the average vectorial velocity.

Amplitude-Duration Relationship—the relationship between the amplitude of the saccade and its duration.

Coefficient of the Amplitude-Duration Relationship. The amplitude-duration relationship varies from person to person, and describes the tendency for saccade duration to increase linearly with amplitude, according to the equation:

Duration=C×|Amplitude|+Duration_(min)

To calculate the slope coefficient of this relationship, a data set may be constructed from the saccade groups such that x-column data contained the larger absolute component (horizontal or vertical) amplitude and y-column data contained the respective saccade duration.

The slope coefficient of the amplitude-duration relationship may be obtained from a linear regression of this data set.

Main Sequence Relationship—the relationship between the amplitude of the saccade and its peak velocity.

Coefficient of the Main Sequence Relationship. The main sequence relationship varies from person to person, and describes the tendency for saccade peak velocity to increase exponentially with amplitude, according to the equation:

${{Peak}\mspace{14mu} {Velocity}} = {{Velocity}_{\max}\left( {1 - ^{\frac{{Amplitude}}{c}}} \right)}$

This relationship has shown to be roughly linear for small saccades in the range of 0-10° amplitude. As a result, a linear approximation may be acceptable in the current context, as the saccades produced during reading are often on the order of 0-3° amplitude, with very few over 10° amplitude.

To calculate the slope coefficient of this relationship, a data set may be constructed from the saccade groups such that x-column data contained absolute component (horizontal or vertical) amplitude and y-column data contained the respective absolute component peak velocity. The slope coefficient of the main sequence relationship may be obtained from a linear regression of this data set.

Characteristics processed by the aggregated scanpath module 241 may include the following:

Scanpath Length—summated amplitude of all detected saccades. Scanpath length is indicative of the efficiency of visual search, and may be considered as a candidate biometric feature under the assumption that visual search is dependent on the subject's familiarity with similar patterns/content. Scanpath length may be measured as the sum of absolute distances between the vectorial centroid of fixation points, where the vectorial centroid was defined as the Euclidean norm of the horizontal and vertical centroid positions, according to the equation:

Scanpath Length=Σ_(i=2) ^(n)|√{square root over (x _(i) ² +y _(i) ²)}−√{square root over (x _(i-1) ² +y _(i-1) ²)}|

Scanpath Area—area that is defined by a convex hull that is created by fixation points. Scanpath area may be measured as the area of the convex hull formed by fixation points. Scanpath area is similar to scanpath length in its indication of visual search efficiency, but may be less sensitive to localized searching. That is, a scanpath may have a large length while only covering a small area.

Regions of Interest—total number of spatially unique regions identified after applying a spatial mean shift clustering algorithm to the sequence of fixations that define a scanpath

Regions of interest may be measured as the total number of spatially unique regions identified after applying a spatial mean shift clustering algorithm to the fixation points of the scanpath, using a sigma value of 2° and convergence resolution of 0.1°.

Inflection Count—number of eye-gaze direction shifts in a scanpath. Inflections occur when the scanpath changes direction, in reading there are a certain amount of “forced” inflections that may be necessary to progress through the text, but general differences in inflection count are indicative of attentional shifts. Inflection count may be measured as the number of saccades in which the horizontal and/or vertical velocity changes signs, according to the following algorithm:

1. Inflections = 0 2. 1 = 2 3. While i < Saccade Count: 4. If sign(Velocity_(i)) != sign(Velocity_(i−1)): 5. Inflections = Inflections + 1 6. End if 7. i = i + 1 8. End while

Scanpath_fix—aggregated representation of a scanpath that is defined by fixation points and their coordinates.

OPC biometric template 242 and scanpath biometric template 244 may be tested for match/non-match. Characteristics may be compared using Gaussian cumulative distribution function (CDF) 246. In some cases, all characteristics except the scanpath_fix are compared via Gaussian cumulative distribution function (CDF) 246.

To determine a relative measure of similarity between metrics, a Gaussian cumulative distribution function (CDF) was applied as follows, were x and μ are the metric values being compared and σ is the metric-specific standard deviation:

$p = {\frac{1}{\sigma \sqrt{2\pi}}{\int_{- \infty}^{x}{^{\frac{t - \mu}{2\sigma^{2}}}\ {t}}}}$

A Gaussian CDF comparison produces a probability value between 0 and 1, where a value of 0.5 indicates an exact match and a value of 0 or 1 indicates no match. This probability may be converted into a more intuitive similarity score, where a value of 0 indicates no match and values of 1 indicates an exact match, with the following equation:

Similarity=1−|2p−1|

From the similarity score, a simple acceptance threshold may be used to indicate the level of similarity which constitutes a biometric match.

In some embodiments, scanpath_fix characteristics are compared via pairwise distances between the centroids representing positions of fixations at 248. In comparing two scanpaths, the Euclidean pairwise distance may be calculated between the centroid positions of fixations. Following this, a tally may be made of the total number of fixation points in each set that could be matched to within 1° of at least one point in the opposing set. The similarity of scanpaths may be assessed by the proportion of tallied fixation points to the total number of fixation points to produce a similarity score similar to those generated for the various eye movement metrics. In some embodiments, the total difference is normalized to produce a similarity score with a value of 0 indicates no match and values of 1 indicates an exact match.

Iris similarity score 270 may be generated using iris templates 272. In this example, to produce similarity score 270, a Hamming distance calculation is performed at 274.

Periocular similarity score 280 may be generated using periocular templates 282. Periocular similarity score 280 may be based periocular template comparisons at 284.

At 250, weighted fusion module produces a combined similarity score via a weighted sum of similarity scores produced by one or more of the individual metrics. Weights for each individual metrics may be produced empirically. Other score level fusion techniques can be applied, e.g., density-based score fusion techniques, transformation score fusion, classifier-based score fusion, methods that employ user-specific and evolving classification thresholds, and etc. The resulting similarity score may be employed for the decision of match/non-match for scanpath authentication or serves as an input to decision fusion module 222, which may combine, for example, OPC and CEM biometrics.

For example at 222, OPC similarity score 224 and CEM similarity score 226 may be considered for final match/non-match decisions. Match/non-match decisions may be made based on one or more of the following information fusion approaches:

Logical OR, AND. Logical fusion method employs individual decisions from the OPC and scanpath modalities in a form of 1 (match) or 0 (non-match) to produce the final match/non-match decision via logical OR (or AND) operations. In case of OR at least one method should indicate a match for the final match decision. In case of AND both methods should indicate a match for the final match decision.

MIN, MAX. For a MIN (or MAX) method, the smallest (or largest) similarity score may between the OPM and the scanpath modalities. Thresholding may be applied to arrive to the final decision. For example, if the resulting value is larger than a threshold a match is indicated; otherwise, a non-match is indicated.

Weighted addition. Weighted summation of the two or two similarity scores from the OPC, CEM, iris, and periocular may be performed via the formula p=w₁·A+w₂·B+w₃·C+w₄·D. Here p is the resulting score, A, B, C and B stands for scores derived from the OPC, CEM, Iris, and Periocular respectively. w1, w2, w3, w4 are corresponding weights. The resulting score p may be compared with a threshold value. If p is greater than the threshold, a match is indicated; otherwise, a non-match is indicated.

Other score level fusion techniques can be applied, e.g., density-based score fusion techniques, transformation score fusion, classifier-based score fusion, methods that employ user-specific and evolving classification thresholds, and etc.

FIG. 3 is a block diagram illustrating architecture for biometric authentication or/and detection of user's state via oculomotor plant characteristics according to one embodiment. In certain embodiments, assessment using OPC as described in FIG. 3 may be combined with assessments based on CEM, iris characteristics, periocular information, or some or all of those traits. In one embodiment, a biometric authentication is a based on a combination of OPC, CEM, iris characteristics, and periocular information.

Biometric authentication 300 or detection of user's state 300 may engage information during enrollment of a user and, at a later time, authentication of the user or detection of user's state. During the enrollment, the recorded eye movement signal from an individual is supplied to the Eye movement classification module 302. Eye movement classification module 302 classifies the eye position signal 304 into fixations and saccades. A sequence of classified saccades' trajectories is sent to the oculomotor plant mathematical model (OPMM) 306.

Oculomotor plant mathematical model (OPMM) 306 may generate simulated saccades' trajectories based on the default OPC values that are grouped into a vector with the purpose of matching the simulated trajectories with the recorded ones. Each individual saccade may be matched independently of any other saccade. Both classified and simulated trajectories for each saccade may be sent to error function module 308. Error function module 308 may compute error between the trajectories. The error result may trigger the OPC estimation module 310 to optimize the values inside of the OPC vector minimizing the error between each pair of recorded and simulated saccades.

When the minimum error is achieved for all classified and simulated saccade pairs, an OPC biometric template 312 representing a user may be generated. The template may include a set of the optimized OPC vectors, with each vector representing a classified saccade. The number of classified saccades may determine the size of the user's OPC biometric template.

During a person's authentication or/and detection of the state of this person, the information flow may be similar to the enrollment procedure. Eye position data 314 may be provided to eye movement classification module 302. In addition, the estimated user biometrics template may be supplied to the person authentication/state detection module 316 and information fusion module 318 to authenticate a user or/and to determine his/her state. Person authentication module 316 may accept or reject a user based on the recommendation of a given classifier or/and make a decision about user's state. Information fusion module 318 may aggregate information related to OPC vectors. In some embodiments, information fusion module 318 may work in conjunction with the person authentication module to authenticate a person based on multiple classification methods or determine the state of this person. The output during user authentication procedure may be a yes/no answer 320 about claimed user's identity or/and description of what the state that the user is in.

Further description for various modules in this example is provided below.

Eye Movement Classification. An automated eye movement classification algorithm may be used to help establish an invariant representation for the subsequent estimation of the OPC values. The goal of this algorithm is to automatically and reliably identify each saccade's beginning, end and all trajectory points from a very noisy and jittery eye movement signal (for example, as shown in FIG. 4. The additional goal of the eye movement classification algorithm is to provide additional filtering for saccades to ensure their high quality and a sufficient quantity of data for the estimation of the OPC values.

In one embodiment, a standardized Velocity-Threshold (I-VT) algorithm is selected due to its speed and robustness. A comparatively high classification threshold of 70° per second may be employed to reduce the impact of trajectory noises at the beginning and the end of each saccade. Additional filtering may include discarding saccades with amplitudes of less than 4°/s, duration of less than 20 ms, and various trajectory artifacts that do not belong to normal saccades.

Oculomotor Plant Mathematical Model. The oculomotor plant mathematical model simulates accurate saccade trajectories while containing major anatomical components related to the OP. In one embodiment, a linear homeomorphic 2D OP mathematical model is selected. The oculomotor plant mathematical model may be, for example, as described in O. V. Komogortsev and U. K. S. Jayarathna, “2D Oculomotor Plant Mathematical Model for eye movement simulation,” in IEEE International Conference on Biolnformatics and Bioengineering (BIBS), 2008, pp. 1-8. The oculomotor plant mathematical model in this example is capable of simulating saccades with properties resembling normal humans on a 2D plane (e.g. computer monitor) by considering physical properties of the eye globe and four extraocular muscles: medial, lateral, superior, and inferior recti. The following advantages are associated with a selection of this oculomotor plant mathematical model: 1) major anatomical components are accounted for and can be estimated, 2) linear representation simplifies the estimation process of the OPC while producing accurate simulation data within the spatial boundaries of a regular computer monitor, 3) the architecture of the model allows dividing it into two smaller 1D models. One of the smaller models becomes responsible for the simulation of the horizontal component of movement and the other for the vertical. Such assignment, while producing identical simulation results when compared to the full model, may allow a significant reduction in the complexity of the required solution and allow simultaneous simulation of both movement components on a multi-core system.

Specific OPC that may be accounted by the OPMM and selected to be a part of the user's biometric template are discussed below. FIG. 4 illustrates raw eye movement signal with classified fixation and saccades 400 and an associated OPC biometric template 402. In the middle of FIG. 4, simulated via OPMM saccade trajectories generated with the OPC vectors that provide the closest matches to the recorded trajectories are shown.

In this example, a subset of nine OPC is selected as a vector to represent an individual saccade for each component of movement (horizontal and vertical). Length tension (Klt=1.2 g/°)—the relationship between the length of an extraocular muscle and the force it is capable of exerting, series elasticity (Kse=2.5 g/°)—resistive properties of an eye muscle while the muscle is innervated by the neuronal control signal, passive viscosity (Bp=0.06 g·s/°) of the eye globe, force velocity relationship—the relationship between the velocity of an extraocular muscle extension/contraction and the force it is capable of exerting—in the agonist muscle (BAG=0.046 g-s/°), force velocity relationship in the antagonist muscle (BANT=0.022 g-s/°), agonist and antagonist muscles' tension intercept (NFIX_C=14.0 g.) that ensures an equilibrium state during an eye fixation at primary eye position, the agonist muscle's tension slope (NAG_C=0.8 g.), and the antagonist muscle's tension slope (NANT_C=0.5 g.), eye globe's inertia (J=0.000043 g-s²/°). All tension characteristics may be directly impacted by the neuronal control signal sent by the brain, and therefore partially contain the neuronal control signal information.

The remaining OPC to produce the simulated saccades may be fixed to the following default values: agonist muscle neuronal control signal activation (11.7) and deactivation constants (2.0), antagonist muscle neuronal control signal activation (2.4) and deactivation constants (1.9), pulse height of the antagonist neuronal control signal (0.5 g.), pulse width of the antagonist neuronal control signal (PW_(AG)=7+|A| ms.), passive elasticity of the eye globe (Kp=N_(AG) _(_) _(C)−N_(ANT) _(_) _(C)) pulse height of the agonist neuronal control signal (iteratively varied to match recorded saccade's onset and offset coordinates), pulse width of the agonist neuronal control signal (PW_(ANT)=PW_(AG)+6).

The error function module provides high sensitivity to differences between the recorded and simulated saccade trajectories. In some cases, the error function is implemented as the absolute difference between the saccades that are recorded by an eye tracker and saccades that are simulated by the OPMM.

R=Σ _(i=1) ^(n) |t _(i) −s _(i)|

where n is the number of points in a trajectory, t_(i) is a point in a recorded trajectory and s_(i) is a corresponding point in a simulated trajectory. The absolute difference approach may provide an advantage over other estimations such as root mean squared error (RMSE) due to its higher absolute sensitivity to the differences between the saccade trajectories.

First Example of an Experiment with Multimodal Ocular Authentication/Person's State Detection in which Only CEM & OPC Modalities are Employed

The following describes an experiment including biometric authentication/persons's state detection based on oculomotor plant characteristics and complex eye movement patterns.

Equipment. The data was recorded using the EyeLink II eye tracker at sampling frequency of 1000 Hz. Stimuli were presented on a 30 inch flat screen monitor positioned at a distance of 685 millimeters from the subject, with screen dimensions of 640×400 millimeters, and resolution of 2560×1600 pixels. Chin rest was employed to ensure high reliability of the collected data.

Eye Movement Recording Procedure. Eye movement records were generated for participants' readings of various excerpts from Lewis Carroll's “The Hunting of the Snark.” This poem was chosen for its difficult and nonsensical content, forcing readers to progress slowly and carefully through the text.

For each recording, the participant was given 1 minute to read, and text excerpts were chosen to require roughly 1 minute to complete. Participants were given a different excerpt for each of four recording session, and excerpts were selected from the “The Hunting of the Snark” to ensure the difficulty of the material was consistent, line lengths were consistent, and that learning effects did not impact subsequent readings.

Participants and Data Quality. Eye movement data was collected for a total of 32 subjects (26 males/6 females), ages 18-40 with an average age of 23 (SD=5.4). Mean positional accuracy of the recordings averaged between all calibration points was 0.74° (SD=0.54°). 29 of the subjects performed 4 recordings each, and 3 of the subjects performed 2 recordings each, generating a total of 122 unique eye movement records.

The first two recordings for each subject were conducted during the same session with a 20 minute break between recordings; the second two recordings were performed a week later, again with a 20 minute break between recordings.

Performance Evaluation. The performance of the authentication methods was evaluated via False Acceptance Rate (FAR) and False Rejection Rate (FRR) metrics. The FAR represents the percentage of imposters' records accepted as authentic users and the FRR indicates the amount of authentic users' records rejected from the system. To simplify the presentation of the results the Half Total Error Rate (HTER) was employed which was defined as the averaged combination of FAR and FRR.

Performance of authentication using biometric assessment using oculomotor plant characteristics, scanpaths, or combinations thereof, was computed as a result of a run across all possible combinations of eye movement records. For example, considering 3 eye movement records (A, B, and C) produced by unique subjects, similarity scores were produced for the combinations: A+B, A+C, B+C. For the 122 eye movement records, this resulted in 7381 combinations that were employed for acceptance and rejection tests for both methods.

For this experiment, in case of the OPC biometrics, only horizontal components of the recorded saccades with amplitudes>1° and duration over 4 ms were considered for the authentication. As a result average amplitude of the horizontal component prior to filtering was 3.42° (SD=3.25) and after filtering was 3.79° (SD=3.26). Magnitude of the vertical components prior to filtering was quite small (M=1.2° SD=3.16), therefore vertical component of movement was not considered for derivation of OPC due to high signal/noise ratio of the vertical component of movement.

Results. Table I presents results of the experiment described above. In Table I, authentication results are presented for each biometric modality. Thresholds column contains the thresholds that produce minimum HTER for the corresponding authentication approach. CUE refers to counterfeit-resistant usable eye-based authentication, which may include one of the traits, or two or more traits in combination that are based on the eye movement signal.

TABLE I Method Name Thresholds FAR FRR HTER CUE = OPC p_(CUE) = 0.1 30% 24% 27% CUE = CEM p_(CUE) = 0.5 26% 28% 27% CUE = (OPC) OR (CEM) p_(OPC) = 0.8 22% 24% 23% p_(S) = 0.6 CUE = (OPC) AND (CEM) p_(OPC) = 0.1 25% 26% 25.5%  p_(S) = 0.2 CUE = MIN(OPC, CEM) p_(CUE) = 0.1 30% 24% 27% CUE = MAX(OPC, CEM) p_(CUE) = 0.6 25% 20% 22.5%  CUE = w₁□OPC + w₂□CEM p_(CUE) = 0.4 20% 18% 19% CUE = 0.5□(OPC) + 0.5□(CEM) p_(CUE) = 0.4 17% 22% 19.5% 

FIG. 5 is a graph illustrating receiver operating curves (ROC) for ocular biometric methods in the experiment described above. Each of ROC curves 500 corresponds to a different modality and/or fusion approach. Curve 502 represents an authentication based on OPC. Curve 504 represents an authentication based on CEM. Curve 506 represents an authentication based on (OPC) OR (CEM). Curve 508 represents an authentication based on (OPC) AND (CEM). Curve 510 represents an authentication based on MIN (OPC, CEM). Curve 512 represents an authentication based on MAX (OPC, CEM). Curve 514 represents an authentication based on a weighted approach w1*OPC+w2*CEM.

Results indicate that OPC biometrics can be performed successfully for a reading task, where the amplitude of saccadic eye movements can be large when compared to a jumping dot stimulus. In this example, both the OPC and CEM methods performed with similar accuracy providing the HTER of 27%. Fusion methods were able to improve the accuracy achieving the best result of 19% in case of the best performing weighted addition (weight w₁ was 0.45 while weight w₂ was 0.55). Such results may indicate approximately 30% reduction in the authentication error. In a custom case where weights for OPC and scanpath traits are equal, multimodal biometric assessment was able to achieve HTER of 19.5%.

Second Example of an Experiment with Multimodal Ocular Authentication/Person's State Detection in which Only CEM & OPC & Iris Modalities are Employed

The following describes an experiment including biometric authentication/persons's state detection based on oculomotor plant characteristics, complex eye movement patterns, and iris.

Equipment. Eye movement recording and iris capture were simultaneously conducted using PlayStation Eye web-camera. The camera worked at the resolution of 640×480 pixels and the frame rate of 75 Hz. The existing IR pass filter was removed from the camera and a piece of unexposed developed film was inserted as a filter for the visible spectrum of light. An array of IR lights in a form of Clover Electronics IR010 Infrared Illuminator together with two separate IR diodes placed on the body of the camera were employed for better eye tracking. The web-camera and main IR array were installed on a flexible arm of the Mainstays Halogen Desk Lamp each to provide an installation that can be adjusted to a specific user. A chin rest that was already available from a commercial eye tracking system was employed for the purpose of stabilizing the head to improve the quality of the acquired data. In a low cost scenario a comfortable chinrest can be constructed from very inexpensive materials as well. Stimulus was displayed on a 19 inch LCD monitor at a refresh rate of 60 Hz. A web camera and other equipment such as described above may provide a user authentication station at a relatively low cost.

Eye-tracking software. ITU eye tracking software was employed for the eye tracking purposes. The software was modified to present required stimulus and store an eye image every three seconds in addition to the existing eye tracking capabilities. Eye tracking was done in no-glint mode.

Stimulus. Stimulus was displayed on a 19 inch LCD monitor with refresh rate of 60 Hz. The distance between the screen and subjects' eyes was approximately 540 mm. The complex pattern stimulus was constructed that employed the Rorschach inkblots used in psychological examination, in order to provide relatively clean patterns which were likely to evoke varied thoughts and emotions in participants. Inkblot images were selected from the original Rorschach psychodiagnostic plates and sized/cropped to fill the screen. Participants were instructed to examine the images carefully, and recordings were performed over two sessions, with 3 rotations of 5 inkblots per session. Resulting sequence of images was 12 sec. long.

Eye movement data and iris data was collected for a total of 28 subjects (18 males, 10 females), ages 18-36 with an average age of 22.4 (SD=4.6). Each subject participated in two recording sessions with an interval of approximately 15 min. between the sessions.

Results. Weighted fusion was employed to combine scores from all three biometric modalities. The weights were selected by dividing the recorded data randomly into training and testing sets. Each set contained 50% of the original recording. After 20 random divisions the average results are presented by Table II:

TABLE II Training Set - Testing Set - Average Performance Average Performance Method Name FAR FRR HTER FAR FRR HTER Ocular  22%  37% 25.5 26.2% 51.8%  39% Biometrics = OPC Ocular 27.2% 14.3%  20.7% 26.9% 28.9 27.9% Biometrics = CEM Ocular 16.9% 3.2% 10.1% 13.2% 13.9% 13.6% Biometrics = Iris Ocular  5.3% 1.4% 3.4% 7.6% 18.6% 13.1% Biometrics = w₁□OPC + w₂□CEM + w₃□Iris

FIG. 6 illustrates one embodiment of a system for assessing a user. System 600 includes user system 602, computing system 604, network 606, and assessment system 616. User system 602 is connected to user display device 608, user input devices 610, and image sensor 611. Image sensor may be, for example, a web cam. User display device 608 may be, for example, a computer monitor.

Image sensor 611 may sense ocular data for the user, including eye movement and external characteristics, such as iris data and periocular information and provide the information to user system 602. Assessment system 616 may serve content to the user by way of user display device 608. Assessment system 616 may receive eye movement information, ocular measurements, or other information from user system 602. Assessment system 616 may be a biometric assessment system. The biometric assessment system may include a computing device that controls visual or other content served to a user, and assesses eye movements and/or other biometric attributes. The eye movements and other biometric attributes may be assessed by the system in relation to the content and/or stimuli being served and/or other conditions of a user (e.g., the timing of various stimuli in the video content served to a user). In certain embodiments, the assessment system uses non-ocular information relating to the user or the user's environment, in addition to, or instead of, ocular information, to assess a user. Using the information received from user system 602, assessment system 616 may, in various embodiments, assess conditions, characteristics, states, or identity of a user.

In the embodiment shown in FIG. 6, user system 602, computing system 604, and assessment system 614 are shown as discrete elements for illustrative purposes. These elements may, nevertheless, in various embodiments be performed on a single computing system with one CPU, or distributed among any number of computing systems. In certain embodiments, a biometric assessment includes a video-oculography or direct infra red oculography system configured to assess face, iris, and eye movement biometrics.

FIG. 7 illustrates one embodiment of a system for biometric assessment of a user wearing an eye-tracking headgear system. The system may be used, for example, to detect and assess conditions, characteristics, or states of a subject. System 620 may be similar to generally similar to system 600 described above relative to FIG. 6. To carry out an assessment, the user may wear eye tracking device 612. Eye tracking device 612 may include eye tracking sensors for one or both eyes of the user. User system 610 may receive sensor data from eye tracking device 612. Assessment system 616 may receive information from user system 610 for assessing the subject.

Computer systems may, in various embodiments, include components such as a CPU with an associated memory medium such as Compact Disc Read-Only Memory (CD-ROM). The memory medium may store program instructions for computer programs. The program instructions may be executable by the CPU. Computer systems may further include a display device such as monitor, an alphanumeric input device such as keyboard, and a directional input device such as mouse. Computing systems may be operable to execute the computer programs to implement computer-implemented systems and methods. A computer system may allow access to users by way of any browser or operating system.

Embodiments of a subset or all (and portions or all) of the above may be implemented by program instructions stored in a memory medium or carrier medium and executed by a processor. A memory medium may include any of various types of memory devices or storage devices. The term “memory medium” is intended to include an installation medium, e.g., a Compact Disc Read Only Memory (CD-ROM), floppy disks, or tape device; a computer system memory or random access memory such as Dynamic Random Access Memory (DRAM), Double Data Rate Random Access Memory (DDR RAM), Static Random Access Memory (SRAM), Extended Data Out Random Access Memory (EDO RAM), Rambus Random Access Memory (RAM), etc.; or a non-volatile memory such as a magnetic media, e.g., a hard drive, or optical storage. The memory medium may comprise other types of memory as well, or combinations thereof. In addition, the memory medium may be located in a first computer in which the programs are executed, or may be located in a second different computer that connects to the first computer over a network, such as the Internet. In the latter instance, the second computer may provide program instructions to the first computer for execution. The term “memory medium” may include two or more memory mediums that may reside in different locations, e.g., in different computers that are connected over a network. In some embodiments, a computer system at a respective participant location may include a memory medium(s) on which one or more computer programs or software components according to one embodiment may be stored. For example, the memory medium may store one or more programs that are executable to perform the methods described herein. The memory medium may also store operating system software, as well as other software for operation of the computer system.

The memory medium may store a software program or programs operable to implement embodiments as described herein. The software program(s) may be implemented in various ways, including, but not limited to, procedure-based techniques, component-based techniques, and/or object-oriented techniques, among others. For example, the software programs may be implemented using ActiveX controls, C++ objects, JavaBeans, Microsoft Foundation Classes (MFC), browser-based applications (e.g., Java applets), traditional programs, or other technologies or methodologies, as desired. A CPU executing code and data from the memory medium may include a means for creating and executing the software program or programs according to the embodiments described herein.

In some embodiments, collected CEM metrics are treated as statistical distributions, (rather than, for example, processing averages). In some embodiments, fusion techniques, such as random forest, are used.

As used herein, complex oculomotor behavior (“COB”) may be considered as a subtype of basic oculomotor behavior (fixations and saccades). Metrics for COB (which is a part of the Complex Eye Movement Patterns) include simple undershoot or overshoot, corrected undershoot/overshoot, multi-corrected undershoot/overshoot, compound saccades, and dynamic overshoot. In some cases, COB may include variant forms of basic oculomotor behavior, often indicating novel or abnormal mechanics. Examples of different forms of saccadic dysmetria, compound saccades, dynamic overshoot, and express saccades are described below. FIG. 8 is a set of graphs illustrating examples of complex oculomotor behavior.

Saccadic dysmetria is a common occurrence, in which a saccade undershoots or overshoots the target stimulus. Often, if the dysmetria is too large, these saccades are followed by one or more small corrective saccades in the direction of the target. The type of dysmetria may be identified based on these characteristics: undershoot, overshoot, simple (uncorrected), corrected (1 corrective saccade), and multi-corrected (2 or more corrective saccades).

Compound saccades (also referred to as macrosaccadic oscillations) occur as a series of dysmetric saccades around a target. As such, compound saccades may be defined as a series of two or more corrective saccades occurring during a single stimulus, in which the direction of movement changes (undershoot-overshoot-undershoot, overshoot-undershoot-overshoot, etc.)

Dynamic overshoot occurs as a small (0.25° to 0.5° amplitude), oppositely directed, post-saccadic corrective movement. These post-saccadic movements may typically be merged with the preceding saccade. As such, dynamic overshoot may be identified by projecting the absolute distance travelled during the saccade onto the centroid of the previous fixation; if the projected centroid exceeds the post-saccade fixation centroid by more than 0.5° (corresponding to a minimum overshoot of 0.25°), dynamic overshoot occurred may be considered to have occurred.

Express saccades have an abnormally quick reaction time between the appearance of a stimulus and the onset of the saccade. Regular saccades may have a typical latency of 150 milliseconds; as such. As used herein, saccades with latency less than 150 milliseconds may be referred to as “express saccades”.

FIG. 8 present the examples of COB. x-axis=time in milliseconds; y-axis=position in degrees). d, p, q are detection thresholds. Specific numbers relating to COB are provided herein for illustrative purposes. The COB metrics that numbers may vary from embodiment to embodiment, and spatial and temporal characteristics and the corresponding thresholds may also vary from embodiment to embodiment. In various embodiments, COB (for example, the frequency of the occurrence of various metrics that compose COB) is applied for the purposes of liveness testing, detection of the physical and the emotional state of the user of the biometric system, or both.

Biometric Liveness Testing

As used herein, a “biometric liveness test” includes a test performed to determine if the biometric sample presented to a biometric system came from a live human being. In some embodiments, a biometric liveness test is performed to determine if the biometric sample presented to the system is a live human being and is the same live human being who was originally enrolled in the system (the “authentic live human being”).

In various embodiments, liveness detection built upon ocular biometrics framework is used to protect against spoof attacks. Some examples of liveness detection in response to spoofing techniques are described below. Although many of the embodiments are described for detecting to a particular spoofing technique, any of the embodiments may be applied to detect any spoofing technique.

Spoofing Example 1. Spoofing is Done by High-Quality Iris Image Printed on Placard, Paper, Etc. And Presented to the Biometric System for the Authentication or Identification

In this case, CEM (including COB) and OPC eye movement metrics are estimated. CEM related metrics may include fixation count, average fixation duration, average vectorial average vertical saccade amplitude, average vectorial saccade velocity, average vectorial saccade peak velocity, velocity waveform (Q), COB related metrics—undershot/overshoot, corrected undershoot/overshoot, multi-corrected undershoot/overshoot, dynamic, compound, express saccades, scanpath length, scanpath area, regions of interest, inflection count, and slope coefficients of the amplitude-duration and main sequence relationships; OPC—related length tension, series elasticity, passive viscosity of the agonist and the antagonist muscle, force velocity relationship, the agonist and the antagonist muscles' tension intercept, the agonist muscle's tension slope, the antagonist muscle's tension slope, eye globe's inertia, or combinations of one or more of the above. Principal component analysis and/or linear/non-linear discriminant analysis may be performed. The values of the metrics may be compared to the normal human data via statistical tests (for example, t-test, Hoteling's T-square test, MANOVA). From this analysis, a determination is made of whether a presented biometric sample is a fake or it comes from the live-authentic user.

When the spoof is presented, extracted eye metrics may have abnormal values such as zero, or be negative, or, for example, would have a linear form, when non-linear form is the norm. Abnormality examples: a) only a single fixation is detected during template acquisition and/or fixation coordinates may indicate that it is directed outside of the screen boundaries, b) no saccades are detected or saccades have the amplitudes close to zero, c) extracted OPC and CEM characteristics have abnormally small or large values.

In some embodiments, once the biometric sample presented to a biometric system is determined to have come from a live human being, a liveness test is used to determine whether the identified person is live human being who was originally enrolled in the system. Person identification of subject may be performed, for example, as described above relative to FIG. 2.

Spoofing Example 2 Spoofing is Done by Pre-Recording Eye Movement Pattern on the Video Recording Device Such as Camera, Phone, Tablet, Etc

In some embodiments, OPC and CEM modalities are used to extract corresponding metrics The combination of OPC and CEM may be used even in cases when fully random stimulus is presented to the user for authentication/identification, for example, a point of light that is jumping to the random locations on the screen. Each time the pattern of what is presented to the user for authentication/identification may be different, but the person may still able to be identified by the ocular biometric system (for example, the system described in paragraph above relative to FIG. 2). Random characteristics of the stimuli may include spatial location of the presented target (for example, coordinates on the screen) and temporal pattern (for example, the time when each specific jump of the target is presented). However, if the pre-recorded sequence is presented there will be a clear spatial and temporal difference between the behavior of the stimulus and what was pre-recorded.

FIG. 9 illustrates a spoof attack via pre-recorded signal from the authentic user. In the example shown in FIG. 9, the difference between the estimated eye gaze locations from the pre-recorded signal of the authentic user (spoof) and the locations of the stimulus that may be presented during an authentication session. In this example, an intruder puts a pre-recorded video of the eye movements of the authentic user to the sensor. The biometric system randomly changes presented pattern and the estimations of the eye gaze locations from pre-recorded video miss the targets by large margins. In FIG. 9, spatial differences may be readily observed. Solid line dots 700 represent locations of points that were actually presented to the user. Broken line dots 702 represent estimated eye gaze locations that were estimated by processing pre-recorded eye movements of the authentic user to previous recorded sessions. Arrows between the pairs of dots represent positional differences between what was presented and recorded. In this case, large differences clearly indicate that the presented sample is a spoof. In some embodiments, spatial differences are checked as a Euclidian distance metric between the presented locations and recorded from the user.

In case of the spoof (pre-recorded eye movement sequence) the spatial and temporal difference may be large, which allows an easy distinction between the spoof and the authentic signal. For example, FIG. 10 illustrates the same figure for the authentic user. Solid line dots 704 represent locations of points that were actually presented to the user. Broken line dots 706 represent estimated eye locations from an authentic user. In the example illustrated in FIG. 10, an authentic user goes through the authentication process. Small positional differences indicate that the recorded eye is able to follow presented random stimulus and therefore it is not a pre-recorded presentation. Estimated eye gazes from the user fall very closely to presented targets, identifying that a live eye is following the targets. Thus, comparing FIG. 9 and FIG. 10, the distances between the estimated eye gazes of the spoof and what is presented as a stimulus are large, while the differences between the estimated eye gazes from the live user and the stimulus locations are small.

In some embodiments, a similar approach to biometric authentication may be applied in the time domain (for example, for biometric authentication using video). The timings of the appearances of flashing dots can be randomized and in this case pre-recorded eye movements may be out of sync temporally with what is presented on the screen, introducing large differences between stimulus onsets the movements that are pre-recorded in the video sequence.

Spoofing Example 3. Spoofing is Done by an Accurate Mechanical Replica of the Human Eye

In some embodiments, differences in the variability between the replica and the actual system are employed for spoof detection. To capture the variability differences between live and spoof, covariance matrixes may be built based on the OPC values estimated by an OPC biometric framework. Once such matrixes are constructed, a Principal Component Analysis (PCA) may be performed to select a subset of characteristics that contain the bulk of the variability. The resulting OPC subset may be employed to compute corresponding vector of eigen values. To make a decision if specific sample is live or a spoof, the maximum eigen value in the vector may be compared to a threshold. When a value exceeds a threshold the corresponding biometric template is marked as a spoof. If the value is less than or equal to the threshold, the corresponding biometric template may be marked as live.

In the case when an intruder steals the biometric database and performs spoofing with the mechanical replica of the eye created with the knowledge of the user's biometric template, the Correct Recognition rate (correct rate of the identification of the spoof or live sample) may be approximately 85%.

In certain embodiments, a linear discriminant analysis (LDA) is performed to determine the liveness based on the metrics using the OPC biometric template. In certain embodiments, a multivariate analysis of variance (MANOVA) is performed to determine the liveness based on the metrics using the OPC biometric template.

Spoofing Example 4. Spoofing is Done by Imprinting High-Quality Iris Image on a Contact Lens and Putting on Top of the Intruders Live Eye

In a case when the iris part of the ocular biometrics system is spoofed by a contact lenses with imprinted pattern of the authentic user, the ocular biometric system may use other modalities such as OPC, CEM, and periocular features to make a distinction about the authenticity of the user. Biometric performance of all biometric modalities other than the iris may be used to determine the authenticity of the user in the case when iris modality is completely spoofed.

In some embodiments (including, for example, the embodiments described above relative to Spoofing Examples 1-4), once the biometric sample presented to a biometric system is determined to have come from a live human being, a liveness test may be used to determine whether the identified person is live human being who was originally enrolled in the system. Person identification of subject may be performed, for example, as described above relative to FIG. 2.

In some embodiments, a user indicates a coercion attack to a system via eye movement patterns. The eye movements may be pre-established before the coercion attack (for example, during training of the user). Signals by a user using eye movement patterns may be done covertly or overtly. Signals by the user to ocular biometrics system via eye tracking may be hard to detect by an intruder and will be non-intrusive. The eye tracking technology may be able to detect the direction of gaze with a precision of approximately 0.5° of the visual angle. A human, while able to tell the general location of the eye gaze and possibly count the amount of gaze shifts, cannot distinguish precisely where someone is looking.

Different types of authentication/identification stimuli such as images can be employed to allow the user to signal coercion attack in various embodiments. For example, the following types of images may be employed: a) images containing a significant amount of rich textural information across the entire image, e.g., a forest or hills, b) images containing several separate zones of attention, e.g., structures, buildings, c) images with artificial content highlighting well defined focal points, e.g., blue and red balloons.

In various examples given below, each presented image type may facilitate a login process that would allow the user to fixate his/her eyes on the distinct focal points presented on the image to signal “normal” or “coercion” attack. For example, if the image of mountains is presented during “normal” login, a user will look at the base of the hills, whereas during “coercion” entry the user will look at the pine trees.

Difference in shapes (for example, scanpaths) as drawn by the eyes (i.e. spatial and temporal differences in the eye movement signatures) may be used to determine the difference between the “coercion” and “normal login”. Examples are provided below.

FIG. 11 illustrates an example of the difference between “normal” and “coercion” logins. The light shaded scanpath indicates the scanpath for normal login. The darker scanpath indicates the coercion login. Circles represent fixations and lines represent saccades. The spatial locations of the scanpaths may be different, however the number of fixations is the same. The intruder would not be able to notice the difference between spatial locations, because the gaze would be directed on the same screen, in the general vicinity of the presented picture. Also counting the change of the direction of the eye movement would not help, because both scanpaths have the same number of fixations and saccades that compose them.

Similarly to FIG. 11, FIG. 12 illustrates an example of the difference between “normal” and “coercion” logins. The light shaded scanpath indicates the scanpath for normal login. The darker scanpath indicates the coercion login. Circles represent fixations and lines represent saccades. The spatial locations of the scanpaths are different, however the number of fixations is the same.

It is noted that even if an intruder hacks/steals the database of the biometric templates of the system users and, for example, if the intruder knows that user has to make four fixations and four saccades to log into the system, the information would not help the intruder to detect whether the user has executed the “coercion” sequence, because this sequence also contains four fixations and four saccades and by visually observing the eye movements it would be impossible to determine which sequence a user actually executes. The intruder might count the number of rapid rotations of the eye (saccades), but not the spatial locations of the resulting fixations.

Detection of the Physical and Emotional State of the Subject

An ocular biometrics system may provide information and services in addition to determining the identity of a user. In some embodiments, the system is used to acquire information about the state of the subject. In various embodiments, indicators of the physical, emotional, health state, or whether a user is under the influence of alcohol/drugs, or a combination thereof, may be assessed.

In one embodiment, a system detects exhaustion of a user. Exhaustion detection may be beneficial to systems that are installed in user-operated machines such as cars, planes etc. In addition to the user's identity, the system may detect fatigue and warn the user against operating machinery in such a state.

In an embodiment, an ocular biometric system detects, and assesses the severity of a traumatic brain injury or a brain trauma such as a concussion of the soldiers on the battlefield or from a sports injury (for example, when a soldier is injured as a result of the explosion or some other occurrence).

Examples of states that may be detected using an ocular biometric system include emotional states and physical states including, excessive fatigue, brain trauma, influence of substances or/and drugs, high arousal.

In some embodiments, metrics that are contained in OPC, CEM (including COB) categories are employed to detect the normality of a newly captured template. For example iris modality, periocular modality, OPC modality may indicate that user A is trying to authenticate into the system. However, metrics in the COB category may indicate excessive amount, of undershoots, overshoots, or corrective saccade. This might be the case of the excessive fatigue, because such “noisy” performance of the Human Visual System is indicative of tiredness. Fatigue may be also indicated by larger than normal amounts of express saccades and non-normal saccades in terms of their main-sequence curve (i.e., saccade will have smaller maximum velocity than during a normal saccade).

Cases of brain trauma may be detected as excessive variability present in the metrics, for example, in values of the COB metrics. Statistical tools as linear/non-linear discriminant analysis, principal component analysis, MANOVA, and other tests statistical tests may be employed to detect this excessive variability and make a decision about brain trauma. Maintaining a steady fixation against a stationary target and accurately following smooth moving target may be employed for the brain trauma detection. In such cases distance and velocity metrics may be used to determine how well the target is fixated and how closely the smoothly moving target is tracked.

Substance influence such as alcohol and drugs may be also determined by statistically processing the metrics in the CEM and OPC templates. For example number of fixations and fixation durations (both metrics are part of the CEM template) might be increased when a user is under the influence of drugs/alcohol when these metrics are compared to the already recorded values.

In case of emotion detection such as arousal fixation duration might be longer than normal, large amounts of fixations might be exhibited.

The case of excessive fatigue, brain trauma, influence of substances or/and drugs may be distinguished from the failure of liveness test. In case of user exhaustion the ocular biometric system would extract OPC, CEM (including COB) metrics, or combinations thereof, and their corresponding range would be close to normal values, even if the values are close to the top of the normal range. Extracted metrics that would fail the liveness test would likely have abnormal values, for example, negative, constant, close to zero, or values that are extremely large.

Biometric Identification Via Miniature Eye Movements

In some embodiments, a system performs biometric identification using miniature eye movements. Biometric identification via miniature eye movements may be effected when a user is fixated just on a single dot. An eye movement that is called an eye fixation may be executed. Eye fixation may include three miniature eye movement types: tremor, drift, and micro-saccades (saccades with amplitudes of 0.5°). Assuming high positional and temporal resolution of an eye tracker, OPC and CEM metrics may be extracted from the micro saccades as from saccades with amplitudes larger than 0.5°. In addition, tremor characteristics such as frequency and amplitude may be employed for the person identification/authentication. Drift velocity and positional characteristics may also be employed for the person identification/authentication. In some embodiments, biometric identification via miniature eye movements is performed by the same CEM modules and is included in the regular CEM biometric template.

Biometric Identification Via Saliency Maps

In some embodiments, a saliency map is generated based on recorded fixations. As used herein, a “saliency map” is a topographically arranged map that represents visual saliency of a corresponding visual scene.” Fixation locations may represent highlights of the saliency maps or probabilistic distributions depending on the implementation. In the case of a static image, all fixation locations may be employed to create nodes in the saliency map. In case of the dynamic stimuli, such as video, recorded fixations may be arranged in sliding temporal windows. A separate saliency may be created for each temporal window. Saliency maps (for example, driven by the fixations and/or other features of the eye movement signal) may be stored as a part of an updated CEM template (for example, based on the approach described in FIG. 13) may be compared by statistical tests, such as Kullback-Leibler, to determine the similarity between the templates. The similarities/differences between the templates may be used to make decision about the identity of the user.

Biometric Assessment with Subject State Detection

FIG. 13 illustrates biometric assessment with subject state detection and assessment. As used herein, “subject state characteristic” includes any characteristic that can be used to assess the state of a subject. States of a subject for which characteristics may be detected and/or assessed include a subject's physical state, emotional state, condition (for example, subject is alive, subject is under the influence of a controlled substance), or external circumstances (for example, subject is under physical threat or coercion). Many of the aspects of the assessment approach shown in FIG. 13 may be carried out in a similar manner to that described above relative to FIG. 2. At 720, after biometric template generation but before biometric template matching via individual traits, state subject detection may be performed (for example, to conduct detection related to liveness, coercion, physical, emotional, health states, and the detection of the influence of the alcohol and drugs.)

In some embodiments, a decision fusion module (for example, as represented by fusion module 222 shown in FIG. 13) may perform also a liveness check in a case when one of the modalities gets spoofed (for example, the iris modality gets spoofed by the contact lens with imprinted iris pattern.)

In some embodiments, a system for person identification with biometric modalities with eye movement signals includes liveness detection. Liveness detection may include estimation and analysis of OPC. In some embodiment liveness detection is used to prevent spoof attacks (for example, spoof attacks that including generating an accurate mechanical replica of a human eye.) Spoof attack prevention may be employed for one following classes of replicas: a) replicas that are built using default OPC values specified by the research literature, and b) replicas that are built from the OPC specific to an individual.

In some embodiments, oculomotor plant characteristics (OPC) are extracted and a decision is made about the liveness of the signal based on the variability of those characteristics.

In some embodiments, liveness detection is used in conjunction with iris authentication devices is deployed in remote locations with possibly little supervision during actual authentication. Assuming that OPC capture is enabled on the existing iris authentication devices by a software upgrade such devices will have enhanced biometrics and liveness detection capabilities.

In some embodiments, a mathematical model of the oculomotor plant simulates saccades and compares them to the recorded saccades extracted from the raw positional signal. Depending on the magnitude of the resulting error between simulated and recorded saccade, an OPC estimation procedure may be invoked. This procedure refines OPC with a goal of producing a saccade trajectory that is closer to the recorded one. The process of OPC estimation may be performed iteratively until the error is minimized. OPC values that produce this minimum form become a part of the biometric template, which can be matched to an already enrolled template by a statistical test (e.g. Hotelling's T-square). Once two templates are matched, the resulting score represents the similarity between the templates. The liveness detection module checks the liveness of a biometric sample immediately after the OPC template is generated. A yes/no decision in terms of the liveness is made.

The modules used for the procedures in FIG. 13 may be implemented in a similar manner to those described relative to FIG. 2. A liveness detector and oculomotor plant mathematical models that can be employed for creating a replica of a human eye in various embodiments are described below.

Liveness Detector

The design of a liveness detector has two goals: 1) capture the differences between the live and the spoofed samples by looking at the variability of the corresponding signals, 2) reduce the number of parameters participating in the liveness decision.

Collected data indicates the feasibility of the goal one due to the substantial amount of the variability present in the eye movement signal captured from a live human and relatively low variability in the signal created by the replica. In addition to what was already stated previously about the complexity of the eye movement behavior and its variability. It is noted that the individual saccade trajectories and their characteristics may vary (to a certain extent) even in cases when the same individual makes them. This variability propagates to the estimated OPC, therefore, providing an opportunity to assess and score liveness.

To capture the variability differences between live and spoofed samples covariance matrixes may be built based on the OPC values estimated by the OPC biometric framework. Once such matrixes are constructed a Principal Component Analysis (PCA) is performed to select a subset of characteristic that contains the bulk of the variability. A resulting OPC subset is employed to compute corresponding vector of eigen values. To make a decision if specific sample is live or a spoof the maximum eigen value in the vector is compared to a threshold. When a value exceeds a threshold the corresponding biometric template is marked as a spoof and live otherwise.

Operation Modes of Eye Movement-Driven Biometric System

1. Normal Mode

In some embodiments, a video-based eye tracker is used as an eye tracking device. For each captured eye image, a pupil boundary and a corneal reflection from an IR light by the eye tracker are detected to estimate user's gaze direction.

During normal mode of operation of an eye movement-driven biometric system, a user goes to an eye tracker, represented by an image sensor and an IR light, and performs a calibration procedure. A calibration procedure may include a presentation of a jumping point of light on a display preceded by the instructions to follow the movements of the dot. During the calibration eye tracking software builds a set of mathematical equations to translate locations of eye movement features (for example, pupil and the corneal reflection) to the gaze coordinates on the screen.

The process of the biometric authentication may occur at the same time with calibration. Captured positional data during calibration procedure may be employed to verify the identity of the user. However, a separate authentication stimulus may be used following the calibration procedure if employment of such stimulus provides higher biometric accuracy.

2. Under Spoof Attack

To initiate a spoof attack, an attacker presents a mechanical replica to the biometric system. The eye tracking software may detect two features for tracking—pupil boundary and the corneal reflection. The replica follows a jumping dot of light during the calibration/authentication procedure. The movements of the replica are designed to match natural behavior of the human visual system. A template may be extracted from the recorded movements. A liveness detector analyzes the template and makes a decision if corresponding biometric sample is a spoof or not.

Mathematical Models of Human Eye

The eye movement behavior described herein is made possible by the anatomical structure termed the Oculomotor Plant (OP) and is represented by the eye globe, extraocular muscles, surrounding tissues, and neuronal control signal coming from the brain. Mathematical models of different complexities can represent the OP to simulate dynamics of the eye movement behavior for spoofing purposes. The following describes three OP models that may be employed in various embodiments.

Model I. Westheimer's second-order model represents the eye globe and corresponding viscoelasticity via single linear elements for inertia, friction, and stiffness. Individual forces that are generated by the lateral and medial rectus are lumped together in a torque that is dependent on the angular eye position and is driven by a simplified step neuronal control signal. The magnitude of the step signal is controlled by a coefficient that is directly related to the amplitude of the corresponding saccade.

OPC employed for simulation. Westheimer's model puts inertia, friction, and stiffness in direct dependency to each other. In the experiments described herein, only two OPC—stiffness coefficient and step coefficient of the neuronal control signal—were varied to simulate a saccade's trajectory.

Model II. A fourth-order model proposed by Robinson employs neuronal control signal in a more realistic pulse-step form, rather than simplified step form. As a result the model is able to simulate saccades of different amplitudes and durations, with realistic positional profiles. The model breaks OPC into two groups represented by the active and passive components. The former group is represented by the force-velocity relationship, series elasticity, and active state tension generated by the neuronal control signal. The latter group is represented by the passive components of the orbit and the muscles in a form of fast and slow viscoelastic elements. All elements may be approximated via linear mechanical representations (for example, linear springs and voigt elements.)

OPC employed for simulation. In experiments described herein, the following six parameters were employed for saccade's simulation in the representation: net muscle series elastic stiffness, net muscle force-velocity slope, fast/slow passive viscoelastic elements represented by spring stiffness and viscosity.

Model III is a fourth-order model by Komogortsev and Khan, which is derived from an earlier model of Bahill. This model represents each extraocular muscle and their internal forces individually with a separate pulse-step neuronal control signal provided to each muscle. Each extraocular muscle can play a role of the agonist—muscle pulling the eye globe and the antagonist—muscle resisting the pull. The forces inside of each individual muscle are: force-velocity relationship, series elasticity, and active state tension generated by the neuronal control signal. The model lumps together passive viscoelastic characteristics of the eye globe and extraocular muscles into two linear elements. The model is capable of generating saccades with positional, velocity, and acceleration profiles that are close to the physiological data and it is able to perform rightward and leftward saccades from any point in the horizontal plane.

OPC extracted for simulation: In experiments described herein, eighteen OPC were employed for the simulation of a saccade: length tension relationship, series elasticity, passive viscosity, force velocity relationships for the agonist/antagonist muscles, agonist/antagonist muscles' tension intercept, the agonist muscle's tension slope, and the antagonist muscle's tension slope, eye globe's inertia, pulse height of the neuronal control signal in the agonist muscle, pulse width of the neuronal control signal in the agonist muscle, four parameters responsible for transformation of the pulse step neuronal control signal into the active state tension, passive elasticity.

Experiment with Human Eye Replicas

Spoof attacks were conducted by the mechanical replicas simulated via three different mathematical models representing human eye. The replicas varied from relatively simple ones that oversimplify the anatomical complexity of the oculomotor plant to more anatomically accurate ones. Two strategies were employed for the creation of the replicas. The first strategy employed values for the characteristics of the oculomotor plant taken from the literature and the second strategy employed exact values of each authentic user. Results indicate that a more accurate individualized replica is able to spoof eye movement-driven system more successfully, however, even in this error rates were relatively low, i.e., FSAR=4%, FLRR=27.4%.

For spoofing purposes, a replica was made to exhibit most common eye movement behavior that includes COB events. These events and their corresponding parameters are illustrated by FIG. 8 and described below.

In this example, the onset of the initial saccade to the target occurs in a 200-250 ms temporal window, representing typical saccadic latency of a normal person. Each initial saccade is generated in a form of undershoot or overshoot with the resulting error of random magnitude (p2) not to exceed 2° degrees of the visual angle. If the resulting saccade's offset (end) position differs from the stimulus position by more than 0.5° (p3) a subsequent corrective saccade is executed. Each corrective saccade is performed to move an eye fixation closer to the stimulus with the resulting error (p4) not to exceed 0.5°. The latency (p5) prior to a corrective saccade is randomly selected in a range 100-130 ms. The durations of all saccades is computed via formula 2.2 DOT A+21, where A represents saccade's amplitude in degrees of the visual angle.

To ensure that spoofing attack produces accurate fixation behavior following steps are taken: 1) random jitter with amplitude (p6) not to exceed 0.05° is added to simulate tremor, 2) blink events are added with characteristics that resemble human behavior and signal artifacts produced by the recording equipment prior and after blinks. The duration (p7) of each blink is randomly selected from the range 100-400 ms. Time interval between individual blinks is randomly selected in the 14-15 sec. temporal window. To simulate signal artifacts introduced by the eye tracking equipment prior and after the blink, the positional coordinates for the eye gaze samples immediately preceding and following a blink are set to the maximum allowed recording range (±30° in our setup).

During a spoof attack, in this experiment, only horizontal components of movement are simulated. While generation of vertical and horizontal components of movement performed by the HVS can be fully independent, it is also possible to witness different synchronization mechanisms imposed by the brain while generating oblique saccades. Even in cases when a person is asked to make purely horizontal saccades it is possible to detect vertical positional shifts in a form of jitter and other deviations from purely horizontal trajectory. Consideration and simulation of the events present in the vertical component of movement would introduce complexity into the modeling process.

The goal of the stimulus was to invoke a large number of horizontal saccades to allow reliable liveness detection. The stimulus was displayed as a jumping dot, consisting of a grey disc sized approximately 1° with a small black point in the center. The dot performed 100 jumps horizontally. Jumps had the amplitude of 30 degrees of the visual angle. Subjects were instructed to follow the jumping dot.

Two strategies that may be employed by an attacker to generate spoof samples via described oculomotor plant models as described as follows: The first strategy assumes that the attacker does not have access to the stored OPC biometric template data. In this case the attacker employs the default OPC values taken from the literature to build a single mechanical replica of the eye to represent any authentic user. The second strategy assumes that the attacker has stolen the database with stored OPC biometric templates and can employ OPC values to produce a personalized replica for each individual to ensure maximum success of the spoof attack. In this case a separate replica is built for each individual by employing OPC averages obtained from the OPC biometric templates generated from all recordings of this person.

As a result the following spoofing attacks were considered. Spoof I-A and Spoof II-A represent the attacks performed by the replica created by the Model I and Model II respectively employing the first spoof generation strategy. Spoofs for the Models I and II created by the second strategy (i.e., Spoofs I-B, II-B), were not considered because if the corresponding OPC for the model I and II are derived from the recorded eye movement signal, then the saccades generated with resulting OPC are very different from normally exhibited saccades. Model III allows creating human-like saccades for both strategies, therefore producing attacks Spoof III-A and III-B.

The following metrics are employed for the assessment of liveness detection and resistance to spoofing attacks.

$\begin{matrix} {{C\; R} = {100 \cdot \frac{CorrectlyClassifiedSamples}{TotalAmountOfSamples}}} & 1 \end{matrix}$

Here CR is Classification Rate. CorrectlyClassifiedSamples is the number of tests where OPC set was correctly identified as spoof or live. TotalAmountOfSamples is the total number of classified samples.

$\begin{matrix} {{F\; S\; A\; R} = {100 \cdot \frac{ImproperClassifiedSpoofSamples}{TotalAmountOfSpoofSamples}}} & 2 \end{matrix}$

Here FSAR is False Spoof Acceptance Rate. ImproperClassifiedSpoofSamples is the number of spoof samples classified as live and TotalAmountOfSpoofSamples is the total amount of spoofed samples in the dataset.

$\begin{matrix} {{F\; L\; R\; R} = {100 \cdot \frac{ImproperClassifiedLiveSamples}{TotalAmountOfLiveSamples}}} & 3 \end{matrix}$

Here FLRR is False Live Rejection Rate. ImproperClassifiedLiveSamples is the number of live samples that was marked by liveness detector as a spoof and TotalAmountOfLiveSamples is the total amount of live records in the dataset.

Table III shows results of the spoof detection experiment. Numbers in the table represent percentages. “SD” represents standard deviation. The signal from live humans was captured at 1000 Hz with a high-grade commercial eye tracking equipment, providing an opportunity to obtain the OPC from a very high quality eye positional signal. The signal from the replica was generated also at a frequency of 1000 Hz.

TABLE III Spoof CR (SD) FSAR (SD) FLRR (SD) EER I-A  93 (3.9) 0 (0)  7.4 (4.1) 5 II-A  80.3 (25.2) 0 (0) 11.8 (7)  8 III-A 86.4 (4.2) 0 (0) 15.5 (4.6) 17 III-B 84.7 (4.1)  4 (5.2) 27.4 (4.1) 20

Biometric Assessment Using Statistical Distributions

In some embodiments, biometric techniques using on patterns identifiable in human eye movements are used to distinguish individuals. The distribution of primitive eye movement features is determined using algorithms based on one or more statistical tests. In various embodiments, the statistical tests may include a Ansari-Bradley test, a Mann-Whitney U-test, a two-sample Kolmogorov-Smirnov test, a two-sample t-test, or a two-sample Cramér-von Mises test. Score-level information fusion may be applied and evaluated by one or more of the following: weighted mean, support vector machine, random forest, and likelihood ratio.

The distribution of primitive features inherent in basic eye movements can be utilized to uniquely identify a given individual. Several comparison algorithms may be evaluated based on statistical tests for comparing distributions, including: the two-sample t-test, the Ansari-Bradley test, the Mann-Whitney U-test, the two-sample Kolmogorov-Smirnov test, and the two-sample Cramér-von Mises test. Information fusion techniques may include score-level fusion by: weighted mean, support vector machine, random forest, and likelihood ratio.

CEM Biometric Framework

In one embodiment, a biometric assessment includes sensing, feature extraction, quality assessment matching, and decision making. In one embodiment, different stages of the assessment are carried out in different modules. In one embodiment, a Sensor module processes the eye movement signal, a Feature Extraction module identifies, filters, and merges individual gaze points into fixations and saccades, a Quality Assessment module assesses the biometric viability of each recording, a Matching module generates training/testing sets and compares individual recordings, and a Decision module calculates error rates under biometric verification and identification scenarios. These modules may be as further described below.

Sensor Module

The Sensor module may parse individual eye movement recordings, combining available left/right eye coordinates and removing invalid data points from the eye movement signal. Eye movement recordings are stored in memory as an eye movement database, with the eye movement signal linked to the experiment, trial, and subject that generated the recording.

Feature Extraction Module

The Feature Extraction module may generate feature templates for each record in the eye movement database. Eye movement features are primarily composed of fixations and saccades. The eye movement signal is parsed to identify fixations and saccades using an eye movement classification algorithm, followed by micro-saccade and micro-fixation filters.

Fixation and saccade groups are merged, identifying fixation-specific and saccade-specific features. Fixation features include: start time, duration, horizontal centroid, and vertical centroid. Saccade features include: start time, duration, horizontal amplitude, vertical amplitude, average horizontal velocity, average vertical velocity, horizontal peak velocity, and vertical peak velocity.

Quality Assessment Module

The Quality Assessment may module identify the biometric viability of the generated feature templates. In this context, we utilize the fixation quantitative score, ideal fixation quantitative score, fixation qualitative score, and saccade quantitative score as tentative measure of the quality of features obtained from the recording.

Matching Module

The Matching module compares individual records, generating match scores for various metrics using comparison algorithms that operate on feature templates. In this case, comparison algorithms operate to compare the distribution of fixation- and saccade-based features throughout each record. Match scores from each comparison algorithm are then combined into a single match score with an information fusion algorithm.

The Matching module may partition records, splitting the database into training and testing sets by subject, according to a uniformly random distribution. Comparison and information fusion thresholds and parameters are generated on the training set, while error rates are calculated on the testing set.

Decision Module

The Decision module may calculate error rates for comparison and information fusion under biometric verification and identification scenarios. Under one verification scenario, each record in the testing set may be compared to every other record in the testing set exactly once, and false acceptance rate and true positive rate are calculated at varied acceptance thresholds. Under one identification scenario, every record in the testing set may be compared to every other record in the testing set, and identification rates are calculated from the largest match score(s) from each of these comparison sets.

CEM Biometrics

In some embodiments, the following primitive eye movement may be assessed:

Start time (fixation)

Duration (fixation)

Horizontal centroid (fixation)

Vertical centroid (fixation)

Start time (saccade)

Duration (saccade)

Horizontal amplitude (saccade)

Vertical amplitude (saccade)

Horizontal mean velocity (saccade)

Vertical mean velocity (saccade)

Horizontal peak velocity (saccade)

Vertical peak velocity (saccade)

These features accumulate over the course of a recording, as the scanpath is generated. FIG. 14 illustrates a comparative distribution of fixation over multiple recording sessions. By analyzing the distribution of these features throughout each recording, as shown in FIG. 14, the behavior of the scanpath as a whole may be examined. At the same time, by considering the fixations and saccades that compose the scanpath, signal noise from the raw eye movement signal may be removed, and the dataset reduced to a computationally manageable size.

In some embodiments, to compare the distribution of primitive eye movement features, multiple statistical tests are employed. These statistical tests are applied as a comparison algorithm to the distributions of each feature separately. The information fusion algorithms may be applied to the match scores generated by each comparison algorithm to produce a single match score used for biometric authentication.

The following are some comparison algorithms that may be applied in various embodiments.

(C1) Two-Sample t-Test

The two-sample t-test measures the probability that observations from two recordings are taken from normal distributions with equal mean and variance.

(C2) Ansari-Bradley Test

The Ansari-Bradley test measures the probability that observations from two recordings with similar median and shape are taken from distributions with equivalent dispersion.

(C3) Mann-Whitney U-Test

The Mann-Whitney U-test measures the probability that observations from two recordings are taken from continuous distributions with equal median.

(C4) Two-Sample Kolmogorov-Smirnov Test

The two-sample Kolmogorov-Smirnov test measures the probability that observations from two recordings are taken from the same continuous distribution, measuring the distance between empirical distributions.

(C5) Two-Sample Cramér-von Mises Test

The two-sample Cramér-von Mises test measures the probability that observations from two recordings are taken from the same continuous distribution, measuring the goodness-of-fit between empirical distributions.

The following are some information fusion algorithms that may be applied in various embodiments.

(F1) Weighted Mean

The weighted mean algorithm combines the match scores produced for individual metrics into a single match score on the interval [0, 1]. The genuine and imposter match score vectors of the training set are used to select per-metric weighting which minimizes equal error rate via iterative optimization, and the weighted mean produces a single match score as a linear combination of the match scores for each metric.

(F2) Support Vector Machine

The support vector machine algorithm classifies the match scores produced for individual metrics into a single match score in the set {0, 1}. The support vector machine builds a 7th order polynomial on the genuine and imposter match score vectors of the training set, and match scores are classified by dividing them into categories separated by the polynomial on an n-dimensional hyperplane.

(F3) Random Forest

The random forest algorithm combines the match scores produced for individual metrics into a single match score on the interval [0, 1]. An ensemble of 50 regression trees is built on the genuine and imposter match score vectors of the training set, and the random forest calculates the combined match score based on a set of conditional rules and probabilities.

(F4) Likelihood Ratio

The likelihood ratio algorithm combines the match scores produced for individual metrics into a single match score on the interval [0, ∞). The genuine and imposter match score vectors of the training set are modeled using Gaussian mixture models, and the likelihood ratio is calculated as the ratio of the genuine probability density over the imposter probability density.

Experiment to Evaluate Biometric Techniques

The following describes an experiment to evaluate biometric techniques. Biometric accuracy on both high- and low-resolution eye tracking systems were used. Existing eye movement datasets collected by Komogortsev were utilized for comparative evaluation, with collection methodology in the following subsections.

Eye movement recordings were generated on both high-resolution and low-resolution eye tracking systems using a textual stimulus pattern. The text of the stimulus was taken from Lewis Carroll's poem, “The Hunting of the Snark,” chosen for its difficult and nonsensical content, forcing readers to progress slowly and carefully through the text.

For each recording session, subjects were limited to 1 minute of reading. To reduce learning effects, subjects were given a different excerpt from the text for each recording session and each excerpt was selected to ensure that line lengths and the difficulty of material were consistent. As well, excerpts were selected to require approximately 1 minute of active reading.

Eye movements were processed with the biometric framework described above, with eye movement classification thresholds: velocity threshold of 20°/sec, micro-saccade threshold of 0.5°, and micro-fixation threshold of 100 milliseconds. Feature extraction was performed across all eye movement recordings, while matching and information fusion were performed according to the methods described in herein. To assess biometric accuracy, error rates were calculated under both verification and identification scenarios.

Eye movement recordings were partitioned, by subject, into training and testing sets according to a uniformly random distribution with a ratio of 1:1, such that no subject had recordings in both the training and testing sets. Experimental results are averaged over 80 random partitions for each metric, and 20 random partitions for each fusion algorithm. Scores for the best performing algorithms are highlighted for readability.

1. Verification Scenario

False acceptance rate is defined as the rate at which imposter scores exceed the acceptance threshold, false rejection rate is defined as the rate at which genuine scores fall below the acceptance threshold, and true positive rate is defined as the rate at which genuine scores exceed the acceptance threshold. The equal error rate is the rate at which false acceptance rate and false rejection rate are equal. FIGS. 15A and 15B are graphs of the receiver operating characteristic in which true positive rate is plotted against false acceptance rate for several fusion methods. FIG. 15A is based on high resolution recordings. FIG. 15B is based on low resolution recordings.

2. Identification Scenario

Identification rate is defined as the rate at which enrolled subjects are successfully identified as the correct individual, where rank-k identification rate is the rate at which the correct individual is found within the top k matches. FIGS. 16A and 16B are graphs of the cumulative match characteristic for several fusion methods, in which identification rate by rank is plotted across all ranks. The maximum rank is equivalent to the available comparisons. FIG. 16A is based on high resolution recordings. FIG. 16B is based on low resolution recordings.

OPC Effects of Environment and Stimulus

In one embodiment, a biometric assessment system implements a two-dimensional linear homeomorphic model of the oculomotor plant. In various embodiments, the model may be used in identification, verification, or subject state detection, including, in one embodiment, brain injury detection. The model may be well suited for parallel computation, as the horizontal and vertical components of eye movement can be modeled separately. Parameters of the oculomotor plant model may be referred to as oculomotor plant characteristics (OPC) which describe the physical and neurological properties of the human visual system. In one embodiment, the model has 18 parameters for each direction of movement (in this case, horizontal and vertical):

1. Series Elasticity (AG) [KAG_SE=2.5 g/°]

2. Series Elasticity (ANT) [KANT_SE=2.5 g/°]

3. Length-Tension Relationship (AG) [KAG_LT=1.2 g/°]

4. Length-Tension Relationship (ANT) [KANT_LT=1.2 g/°]

5. Force-Velocity Relationship (AG) [BAG=0.046 g×s/°]

6. Force-Velocity Relationship (ANT) [BANT=0.022 g×s/°]

7. Passive Viscosity [BP=0.06 g×s/°]

8. Tension Slope (AG) [NAG_C=0.8 g]

9. Tension Slope (ANT) [NANT_C=0.5 g]

10. Inertial Mass [J=0.000043 g×s2/°]

11. Activation Time (AG) [τAG_AC=11.7]

12. Activation Time (ANT) [τANT_AC=2.4]

13. Deactivation Time (AG) [τAG_DE=2.0]

14. Deactivation Time (ANT) [τANT_DE=1.9]

15. Tension Intercept [NFIX_C=14.0 g]

16. Neural Pulse (AG) [NAG_SAC=55 g]

17. Neural Pulse (ANT) [NANT_SAC=0.5 g]

18. Neural Pulse Width [PW=6 ms]

The terms AG and ANT refer to agonist and antagonist muscles respectively, where the agonist muscle contracts to rotate the eye globe and the antagonist muscle expands to resist the pull of the agonist, with bracketed terms indicating default parameter values. Values in square brackets represent default model parameters used in the OPC biometric template computation. Series elasticity describes the resistive properties of the extraocular muscles, associated with tendons. The length-tension relationship describes the relationship between the length of the muscle and the force it is capable of exerting.

The force-velocity relationship describes the relationship between the velocity of muscle contraction and the force it is capable of exerting. The tension slope and tension intercept describe the reaction of the muscle to innervation and ensure equilibrium during fixation, respectively. As well, the inertial mass of the eye globe and passive viscosity of the surrounding tissue must be accounted for.

The system may employ a pulse-step representation of the neuronal control signal, in which the pulse indicates the magnitude of the neuronal control signal during saccade and the step indicates magnitude during fixation. The pulse width indicates the duration of the neural pulse, which cannot exceed the duration of the saccade, and requires at least 3 ms for activation/deactivation at the beginning and end of a saccade. Activation and deactivation time describe the time required for changes in the neuronal control signal to propagate through the extraocular muscles.

In some embodiments, parameter estimation seeks to identify the OPC parameters that minimize the difference between the recorded saccade trajectory and the simulated trajectory produced by the model.

The estimation routine may use a Nelder-Mead simplex search algorithm for multi-dimensional unconstrained nonlinear minimization. A vector of OPC parameters may be initialized with realistic default values based on the relevant literature. An error function invokes the oculomotor plant model to simulate a saccadic trajectory for a given set of OPC parameters, and returns the absolute difference between the measured and simulated trajectories.

Based on the Nelder-Mead simplex algorithm, the systems adjusts the vector of OPC parameters, shrinking or expanding the search region for each parameter, in an attempt to minimize the result of the error function, until a predetermined exit criteria is satisfied. Constraints may be imposed on the OPC vector to reduce the search space and prevent unrealistic parameter values.

Fixation Density Mapping

In some embodiments, each eye movement signal is transformed into a time-constrained decomposition by using a probabilistic representation of spatial and temporal features related to eye fixations and called fixation density map (FDM). In various embodiments, FDM may be used in identification, verification, or subject state detection, including, in certain embodiments, brain injury detection, fatigue, or autism.

The basis of the developed scheme lies on an idea that during observation of a complex dynamic visual stimulus (e.g. video sequence), spatial and temporal fixation characteristics would be indicative of the brain activity related to the generation and guidance of visual attention, thus providing an opportunity to identify a person based on such activity. This idea is based on the concept that the brain is responsible for encoding information for “where and how” an eye is going to move given specific stimulus, and thus spatial locations of the fixations, their duration, and order can be employed to decode part of this information. Decoded information forms a biometric template that represents part of the brain activity in a mathematical form. To build this representation we implement a projection of the raw time-sampled eye movement signal into the spatial domain. Then, we construct multi-map biometric templates using generated Fixation Density Maps (FDMs), representing person's attention activity for sequential time intervals. This representation possesses the following important properties: 1) spatial distributions of fixation samples are represented in a robust way, 2) possible overlap effects for attention-drawing regions are diminished, and 3) implicit incorporation of time evolution characteristics for the recorded trajectory is possible.

Spatial projection of the eye movement trajectories is implemented by using the Fixation Density Map (FDM) as a basic structural element. Let us assume that an eye tracking device captures eye movement samples with a sampling frequency fs. Then, for a specific recording time interval T_(int) a FDM can be constructed with the following procedure: for an individual fixation point, let Δθ_(i) (FIG. 1) denote the angle formed by visual axis for the fixation point i with regard to axis direction when it crosses the screen center. If we denote with Δθx_(i), Δθy_(i) the angles corresponding to the horizontal and vertical projection of the visual axis, then using the experimental setting geometry—the viewing distance d_(s) is assumed known and fixed—we may calculate the coordinates for the distances Δx_(i), Δy_(i) from the center as:

Δx _(i)=tan(Δθx _(i)·π/180°)·d _(s)

Δy _(i)=tan(Δθy _(i)·π/180°)·d _(s)

By employing the known values for the stimulus screen dimensions (h_(s), w_(s)) and resolution (h_(p), w_(p)), we may in turn convert the distances from the center to the respective pixel coordinates (x_(i), y_(i)) by using the translation equations:

x _(i)=(w _(s)/2)+(w _(s) /w _(p))·Δx _(i)

y _(i)=(h _(s)/2)+(h _(s) /h _(p))·Δy _(i)

During the next step, we construct a discrete map (DCM) from the fixation samples by representing each sample with a unitary spike in the corresponding pixel location:

DCM(x,y)=Σ_(i=1) ^(K)δ(x−x _(i))(y−y _(i)),K=T _(int)·ƒ_(s)

In order to transform the discrete map into a probabilistic representation, we apply a Gaussian kernel of standard deviation σ and construct the final Fixation Density Map:

FDM(x,y)=DCM(x,y)*G _(σ)(x,y)

A value of σ=0.02 (normalized in map's width) is globally used during our implementation. This value was selected so that it corresponds—under our experimental settings—to the average receptive area of the eye's fovea, which is roughly 1° of visual angle.

A FDM can be regarded as a 2-D imprint of the temporal evolution of attention in the space of the specific visual stimulus. In case of a dynamic stimulus, e.g., a video sequence, there are two basic factors which need to be considered: i) the spatial layout of the visual content changes over time and ii) visual inspection may last for an extended period of time (depending on the video duration). Consequently, if a single FDM is constructed for the representation of eye movements during a long recording, there might be overlapping fixations that complicate analysis. Moreover, a single representation cannot capture temporal eye movement characteristics which represent important information related to the individual guidance of visual attention. To overcome this, we propose a procedure that involves a decomposition of each eye movement signal into parts corresponding to sequential time intervals. For every recording, with a total duration of T_(R), the raw eye movement signal is initially partitioned into n equal-duration (T_(int)) nonoverlapping sequential segments. In our implementation we use an interval of T_(int)=5 seconds. This empirically selected duration ensured that a robust sequence of fixations and saccades is captured without significant overlapping effects for the selected stimulus. A separate FDM is constructed for every segment and the final biometric template is formatted by concentrating all of the constructed FDMs into a multi-map representation. During the template matching phase the respective map components are pairwise compared, therefore, correctly aligning temporal and spatial information encoded in the eye movements by the brain and oculomotor plant.

The map may be constructed by directly employing eye movement samples from a person. Map comparison measures that may be used include similarity metic (SIM), Person's correlation coefficient r (PCC), Kullback-Lebler Divergence (KLD), and Earth Mover's Distance (EMD).

In one embodiment, we decided to develop an Euclidean space embedding procedure for the employed dissimilarity measures, so that we can use them for the comparison of biometric templates. Let us denote D the full S×S matrix that contains the dissimilarity values calculated for S different samples. If D_(L) is the lower triangle of the full matrix, then we can use these values in order to construct a symmetric matrix (Ds):

D _(s) =D _(L)+(D _(L))^(T)−diag(D)

Considering each row of the new matrix as a feature of the dissimilarity space we can calculate their Euclidean distances, resulting thus in a normalized Euclidean distance matrix (D_(Final)) which is symmetrical and has a well-defined upper bound:

${{{D_{Final}\left( {i,j} \right)} = \frac{D_{Eucl}\left( {i,j} \right)}{\max_{i,j}D_{Eucl}}},i,{j = 1},\ldots \mspace{14mu},S}\mspace{14mu}$

with D_(Eucl)=EuclideanDistance(Ds′(Ds)_(T))

This matrix can be used during the classification procedure in our biometric scenarios.

Since biometric templates consist of multiple maps, we need to effectively combine the information coming from the map components that correspond to different time intervals. In the case of FDMs, each component correlates with the spatio-temporal layout of the visual input, making thus a direct combination of information on the feature level infeasible. For this reason, we perform information fusion in the match score level, by combining the generated matching scores for every time interval into a single score which expresses the overall similarity between two templates. In our experiments, we implemented and assessed the following fusion schemes:

(SM) Simple Mean. This is the simplest method that is used for fusing the match score information. A simple linear combination with equal weights is computed over the similarity scores that correspond to different FDMs in order to generate a single matching score in the interval [0,1].

(TM) Weighted Mean. The weighted mean algorithm uses a slightly more complicated approach by utilizing a number of training samples in order to perform an iterative error minimization procedure using the genuine and impostor samples. During this process a vector of different weights is generated and then used for the linear combination of the match scores coming from the corresponding FDMs.

(LR) Likelihood Ratio. This fusion algorithm initially builds a set of Gaussian mixture models by using information from a training set. Subsequently, it generates a model by calculating likelihood ratio of the genuine samples over the imposter samples and uses this model for the combination of the individual match scores into a single value in the interval [0, ∞].

Multi-Fusion Assessment System

In some embodiments, a multi-source system includes a mechanism for the combination of eye movement characteristics extracted using different algorithms (multi-algorithm fusion) under the influence of different visual stimuli (multi-stimulus fusion). In various embodiments, multi-source fusion may be used in identification, verification, or subject state detection, including, in certain embodiments, brain injury detection, fatigue, or autism.

In some embodiments, a biometric assessment system implements multi-source (i.e. multi-stimulus and multi-algorithmic) fusion in the performance of eye movement biometrics. The system includes mechanism for combining of eye movement characteristics extracted using different algorithms (multi-algorithm fusion) under the influence of different visual stimuli (multi-stimulus fusion). In one embodiment, a hierarchical weighted fusion scheme is used for the combination of comparison scores generated by different eye movement-driven algorithms under the influence of diverse visual stimuli.

In some embodiments, a biometric assessment system implements multi source (i.e. multi-stimulus and multi-algorithmic) fusion. A hierarchical weighted fusion scheme is used for the combination of comparison scores generated by different eye movement-driven algorithms under the influence of diverse visual stimuli. The biometric assessment system may implement a weight-training method based on the utilization of information related to the identification performance (e.g., Rank/Rank−1 identification rates) for the extraction of stimulus-specific and algorithm-specific weights.

In one embodiment that includes fixation density mapping, the system extracts features for the representation of the attention dependent strategies reflected by the eye movements in cases of dynamically changing stimulus. The extracted features are activation maps that represent in a probabilistic manner the distributions of fixation points. From every eye movement recording, a FDM biometric template is formed as a sequence of n fixation density maps x_(i) (2-D grayscale images) representing the eye movement activity for different time intervals, X^(FDM)=x₁, x, . . . , x_(n). The number of maps (n) is dynamically selected based on the duration of a movie clip and the time interval duration. In this example, the comparison module in the case of the fixation density mapping is the similarity metric. The scores generated from every fixation density map may be aggregated to form the final comparison scores C_(FDM) that feed the input data of the multi-source fusion scheme.

In some embodiments, fusing the information extracted by different algorithms under the influence of different visual stimuli is implemented as a two-stage procedure. A user may observe a number of different visual stimuli (in this example three stimuli) while an eye tracking system captures the performed eye movements. The visual stimuli are presented sequentially, and they can appear in arbitrary order or even have a time gap between them. The signals corresponding to each specific stimulus are forwarded to the first part of the system, where the multi-stimulus fusion occurs for every single biometric algorithm separately. Each algorithm extracts the corresponding features and forms the biometric templates. Following this, the comparison process takes place and the respective sets of comparison scores generated from the different visual stimuli are fused with the use of stimulus specific weights. The weight-training method may be apt to quantify effectively—both in terms of performance and generalization—the relative contribution of information deriving from different stimuli. In the one implementation, the weight-training method is based on the employment of the ranking identification information.

The second part of the fusion may include multi-algorithmic combination of the comparison scores already subjected to multi-stimulus fusion. In a multi-algorithmic fusion module, the relative contribution of each biometric algorithm (which, in one example, includes, OPC, CEM-B, and FDM) may be quantified via the use of multi-algorithmic weights.

In one embodiment, a fusion scheme is mathematically described using the general formula:

C _(fused)=Σ_(i=1) ^(N) w _(a) _(i) ·ƒ_(n)(S _(i)),S _(i)=Σ_(j=1) ^(M) w _(s) _(i) _(j) C _(i) ^(j)

In this example, the index of the stimulus type is denoted with i and the index of the biometric algorithm with j. The aggregated comparison scores extracted from each individual algorithm i during the presentation of stimulus j are denoted with C_(i) ^(j). Also, the stimulus-specific and algorithm-specific weights are denoted with w_(s i) ^(j) and w_(a i) respectively. The normalization function used during the multi algorithmic combination is denoted with ƒ_(n)( ).

A normalization procedure may be applied to facilitate the effective combination of the comparison scores. The normalization of scores is performed because the different comparison modules employed by each algorithm result into the generation of scores with differences in their distribution and their numerical range. Normalization schemes that may be used include Max Min normalization scheme and the Z-score normalization scheme.

The weighted fusion scheme may allow for the construction of dynamic biometric scenarios. In some embodiments, the contribution of each source of information is quantified separately with the use stimulus-specific and algorithm-specific weights. During the presentation of visual stimuli in a biometric system, the relative duration and/or order of different stimuli can dynamically vary. A user can enroll, for example, by observing the four types of stimulus with equal time durations, e.g. 0.25t_(tot) HOR, 0.25t_(tot) RAN, 0.25t_(tot)TEX, 0.25t_(tot) VID, where t_(tot) is the total duration of presentation. During a subsequent recognition attempt, the relative duration and/or order of stimuli presentation can change (even randomly), 0.4ttot TEX, 0.2ttot RAN, 0.3ttot HOR, 0.3ttot VID. In this case, the biometric system which generates the durations (and/or the order) of stimuli can modulate the stimulus-specific (and/or algorithm-specific) weights correspondingly, increasing thus the possibility of an accurate result. In certain embodiments, systems as described herein allow more than one matchers per algorithm, with the application of the normalization function on both stages of fusion.

Inversely, someone may try to spoof-attack the system, e.g. by recording the eye movements during the initial enrollment and replay the recording during in a next attempt. In this case, the difference in stimulus durations (and/or order) during the next recognition attempt will result in a different modulation of the fusion weights, lowering thus the possibilities of a successful spoofing attempt.

Multi-Modal Methods of Assessing Identity

In an embodiment, a multi-modal method of assessing the identity of a person includes measuring eye movement of the person and measuring characteristics of an iris or/and periocular information of a person. Based on measured eye movements, estimates may be made of characteristics of an oculomotor plant of the person, complex eye movement patterns representing brain's control strategies of visual attention, or both. Complex eye movement patterns may include, for example, a scanpath of the person's eyes including a sequence of fixations and saccades. The person's identity may be assessed based on the estimated characteristics of the oculomotor plant, the estimated complex eye movement patterns, and the characteristics of the iris of the person or/and periocular information. The identity assessment may be used to authenticate the person (for example, to allow the person access to a computer system or access to a facility).

In an embodiment, a method of assessing a person's identity includes measuring eye movements of the person. Based on measured eye movements, estimates are made of characteristics of an oculomotor plant of the person and complex eye movement patterns of the person's eyes. The person's identity may be assessed based on the estimated characteristics of the oculomotor plant and the estimated complex eye movement patterns that are representative of the brain's control strategies of visual attention.

In an embodiment, a method of assessing a person's identity includes measuring eye movements of the person while the person is looking at stimulus materials. In various embodiments, for example, the person may be reading, looking at various pictures, or looking at a jumping dot of light. Estimates of characteristics of an oculomotor plant are made based on the recorded eye movements.

In an embodiment, a system for assessing the identity of a person includes a processor, a memory coupled to the processor, and an instrument (e.g. image sensor such as web-camera) that can measure eye movement of a person and external ocular characteristics of the person (such as iris characteristics or periocular information). Based on measured eye movements, the system can estimate characteristics of an oculomotor plant of the person, strategies employed by the brain to guide visual attention represented via complex eye movement patterns, or both. The system can assess the person's identity based on the estimated characteristics of the oculomotor plant, brain strategies to guide visual attention via complex eye movement patterns, and the external ocular characteristics of the person.

In an embodiment, a method of making a biometric assessment includes measuring eye movement of a subject, making an assessment of whether the subject is alive based on the measured eye movement, and assessing a person's identity based at least in part on the assessment of whether the subject is alive.

In an embodiment, a method of making a biometric assessment includes measuring eye movement of a subject, assessing characteristics from the measured eye movement, and assessing a state of the subject based on the assessed characteristics.

Detection of Brain Injuries with Eye Movement Biometrics

In an embodiment, a system detects mild traumatic brain injury (mTBI) by way of the application of eye movement biometrics. Biometric feature vector may be determined from multiple paradigms. The biometric feature vectors may be evaluated for their ability to differentiate subjects diagnosed with mTBI from healthy subjects. In various embodiments, supervised and unsupervised machine learning techniques are applied. Metrics that may be used for brain injury detection may include, in various embodiments, OPC, CEM-P, CEM-B, COB, micro-eye movement, fixation density mapping, or combinations and fusions based on one or more of these metrics, as further illustrated herein.

In some embodiments, brain injury is assessed using values determined from eye movement. FIG. 17 illustrates one embodiment of assessing brain injuries. At 800, eye movement of a person is measured. The apparatus for measuring eye movement and assessing the person's condition may be, in one embodiment, as in the experiment described below, or as described in FIG. 6 or FIG. 7. The apparatus may detect fixations, saccades, or other characteristics or types of eye movements.

Biometric feature vectors and quality measures may be extracted from each recording. CEM, COB, and OPC eye movement biometric techniques may be related to the conscious behavior of the human visual system. COB techniques may be related to subconscious corrective behavior of the human visual system, and OPC techniques are related to the physical structure of the oculomotor plant

In various embodiments, values are based on measurements of one or more characteristics associated with conscious behavior, such as CEM. In other embodiments, values are based on measurements of one or more characteristics associated with conscious behavior, such as COB. In certain embodiments, values are determined from both characteristics associated with conscious and characteristics associated with subconscious behavior.

At 802, values are determined based on the measured eye movement. The values may be in the form of a feature vector. In some embodiments, the values include one or more measures of quality. Average feature values may be determined for assessment purposes.

OPC, CEM, iris, and periocular information may be acquired and values determined in the manner described above relative to FIGS. 2-5 and 11-14, 15A, 15B, 16A, and 16B. Feature vectors may be examined to identify patterns or clustering that might be utilized to distinguish between mTBI and healthy recordings.

At 804, an assessment is made of whether or not the person has suffered brain injury based on the values. In one embodiment, the assessment is of whether the person has suffered mTBI. mTBI may be assessed in a subject based on one or more values, including, in various embodiments: lower than average values of the fixation quantitative score, fixation count, and multi-corrected undershoot; and higher than average values of fixation duration, vectorial saccade amplitude, simple overshoot, and the agonist muscle activation-time constant, or combinations thereof. Values may be assessed form horizontal stimulus recordings, vertical stimulus recordings, or combinations thereof. mTBI may be indicated if one or more values determined from measured eye movement exceeds a predetermined threshold. Assessments of whether or not the person has suffered brain injury may include supervised or unsupervised learning.

In some embodiments, patterns are detected in the person's eye movements. Changes in ocular behavior over time may be identified.

In certain embodiments, assessments include generating, from the eye movements, one or more brain control strategies in guiding visual attention. Brain strategies may be determined from complex eye movement patterns.

In certain embodiments, values are determined from characteristics of an iris or a periocular region of the eye of the person.

In some embodiments, a cross-validation of two or more measures is performed. Cross-validation may, in some cases, reduce the likelihood of overfitting. Linear separability may be achieved between mTBI and healthy subjects. In some cases using unsupervised techniques, the achievable accuracy was relatively high, resulting in a small amount of false positives that could easily be identified during post-diagnostic screening, even where linear separability is not achieved.

Experiment

Apparatus. Binocular eye movements were recorded using the Eye-Link 1000 eye tracking system, with accuracy of 0.25°-0.5°, resolution of 0.01° RMS, and sampling rate of 1000 Hz. The recordings exhibited an average calibration accuracy of 0.8° (±0.6), with an average data loss of 2.3% (±3.9). The stimulus was presented on a flat screen monitor positioned at distance of 685 mm from the subject, with dimensions of 640×400 mm, and resolution of 2560×1600 pixels. A chin rest was employed to improve stability.

Participants. Eye movement recordings were collected for 32 subjects (26 males, 6 females), ages 18-40 with an average age of 23 (±5.4). Of these, 2 subjects had recently sustained head injuries resulting in mTBI; one subject was recorded the day after the injury and the other was recorded 111 days after the injury. Both mTBI subjects and 27 of the healthy subjects performed 4 recordings per stimulus, and 3 of the healthy subjects performed 2 recordings per stimulus. Subjects were given a 20-minute break between the 1st and 2nd recording session, with 2 weeks between the 2nd and 3rd recording session, and 20 minutes between the 3rd and 4th recording session. mTBI injuries were self-reported by subjects after data collection.

Procedure. High-resolution recordings from an eye movement database were used. Eye movement recordings were generated for two stimuli, designed to evoke fixed-amplitude horizontal (30°) and vertical saccades (20°), at regular 1-second intervals. For both stimuli, a small white dot jumped back and forth on a plain black background, eliciting a saccade with each jump. The amplitude was chosen due to screen constraints and complications associated with separating low-amplitude saccades (less than 1°). Subjects were instructed to follow the white dot with their eyes, with 100 saccades elicited per recording. For each recording session, the horizontal and vertical stimuli were presented approximately 2 minutes apart.

In this experiment, biometric feature vectors and standardized quality measures were extracted from each recording according to the CEM-P, CEM-B, COB, and OPC eye movement biometric techniques. CEM techniques are related to the conscious behavior of the human visual system, COB techniques are related to subconscious corrective behavior of the human visual system, and OPC techniques are related to the physical structure of the oculomotor plant. Average feature values were utilized in the case of CEM-B and OPC, which operate by comparing the distribution of features.

Feature vectors were examined manually to identify patterns or clustering that might be utilized to distinguish between mTBI and healthy recordings. During manual examination of the biometric feature vectors, it was noted that (in both horizontal and vertical stimulus recordings) there was a strong tendency for subjects with mTBI to exhibit: lower than average values of the fixation quantitative score, fixation count, and multi-corrected undershoot; and higher than average values of fixation duration, vectorial saccade amplitude, simple overshoot, and the agonist muscle activation-time constant (the agonist muscle activation-time constant is a constant in the mathematical model of the oculomotor plant responsible for transforming the neuronal control signal into contractile force over time, with respect to the agonist extraocular muscle).

After potentially relevant biomarkers were established, two techniques were implemented to assess the accuracy of mTBI detection with supervised and unsupervised learning techniques. The supervised learning technique utilized a regression SVM with an RBF kernel (gamma=1) applied to the 7 features identified during manual examination. Leave-one-out cross-validation was performed to obtain mTBI detection scores for each recording. The unsupervised learning technique utilized a heuristic method, in which the probability of mTBI was estimated as the percentage of features above or below their respective average.

For each recording session, mTBI detection scores were averaged between the horizontal and vertical stimuli, regression values were binned, and simple thresholding was applied to the mTBI detection scores generated by each algorithm to calculate confusion matrices, sensitivity, specificity, and accuracy.

Results.

Detection Scores. mTBI detection scores are presented as a histogram in FIG. 18 and FIG. 19. FIG. 18 is a histogram presenting mTBI detection scores for mTBI determined using a supervised technique. FIG. 19 is a histogram presenting mTBI detection scores for mTBI determined using an unsupervised technique.

Based on the distribution of detection scores, thresholding was employed to measure the achievable accuracy. For the supervised technique, recordings with detection score≦−0.870 were classified as mTBI. For the unsupervised technique, recordings with detection score≧0.79 were classified as mTBI.

Confusion matrices are presented in FIG. 20 and FIG. 21. FIG. 20 is a confusion matrix for biometric assessment of mTBI from a supervised technique. FIG. 21 is a confusion matrix for biometric assessment of mTBI from an unsupervised technique.

For the supervised technique, these results indicate a potential 100% specificity, 100% sensitivity, and 100% accuracy, and for the unsupervised technique, 89% specificity, 100% sensitivity, and 89% accuracy.

During manual examination of biometric feature vectors, features were identified that might exhibit potential recovery patterns; that is, biometric features in mTBI subjects that changed linearly and consistently over time. In this experiment, while several features were noted during examination, there was no crossover in the biometric features noted for the horizontal and vertical stimulus recordings.

Further, in initial investigations of these techniques, the considered experiments were repeated to include all available biometric features. The inclusion of these extraneous features reduced the overall accuracy of these techniques, enforcing the need for dimensionality reduction, and confirming that mTBI does not affect all aspects of the oculomotor system evenly.

Detection of User Fatigue and/or Autism with Eye Movement Biometrics

In an embodiment, a system detects autism or/and user fatigue by way of the application of eye movement biometrics. Biometric feature vector may be determined from multiple paradigms described above, including OPC, CEM, COB, micro eye movement, FDM, and fusions or combinations thereof. The biometric feature vectors may be evaluated for their ability to detect/assess fatigue or/and detect autism.

In some embodiments, fatigue or/and autism is assessed using values determined from eye movement. FIG. 23 illustrates one embodiment of detecting fatigue. At 800, eye movement of a person is measured. The apparatus for measuring eye movement and assessing the person's condition may be, in one embodiment, as in the experiment described below, or as described in FIG. 6 or FIG. 7. The apparatus may detect fixations, saccades, or other characteristics or types of eye movements.

Biometric feature vectors and quality measures may be extracted from each recording. CEM, COB, and OPC eye movement biometric techniques may be related to the conscious behavior of the human visual system. COB techniques may be related to subconscious corrective behavior of the human visual system, and OPC techniques are related to the physical structure of the oculomotor plant.

At 802, values are determined based on the measured eye movement. The values may be in the form of a feature vector. In some embodiments, the values include one or more measures of quality. Average feature values may be determined for assessment purposes.

In certain embodiments, CEM, OPC, iris, and periocular information is acquired and values determined in the manner described above relative to FIGS. 2-5 and 11-14, 15A, 15B, 16A, and 16B. Feature vectors may be examined to identify patterns that might be utilized to detect fatigue.

At 804, an assessment is made of whether is detected and/or assessed based on the values. In some embodiments, fatigue is assessed based one or more behavioral scores determined based on measured eye movement of the person. Fatigue may be assessed in a subject based on one or more values, including, in various embodiments: fixation quantitative score, fixation count, fixation qualitative score, saccade quantitative score, overshoot (for example, relative to a baseline), undershoot, for example, relative to a baseline), or combinations thereof. Values may be assessed form horizontal stimulus recordings, vertical stimulus recordings, or combinations thereof. Fatigue may be indicated if one or more values determined from measured eye movement exceeds a predetermined threshold.

In some embodiments, patterns are detected in the person's eye movements. Changes in ocular behavior over time may be identified.

In certain embodiments, assessments include generating, from the eye movements, one or more brain control strategies in guiding visual attention. Brain strategies may be determined from complex eye movement patterns.

In certain embodiments, values are determined from characteristics of an iris or a periocular region of the eye of the person.

Characteristics of the detected fixations and saccades including metrics specified above vary substantially depending on a choice of classification algorithms that and corresponding detection thresholds. Metrics may be selected for classification algorithms, classification thresholds and eye movement capturing hardware. In some embodiments, a cross-validation of two or more measures is performed.

Behavioral Scores

Behavioral Scores may represent quantitative and qualitative characteristics of the eye movement behavior. The behavior may be recorded in response to a step-stimulus such as jumping dot of light presented for example during eye tracker's calibration. Behavior scores may, in some embodiments, be computed as described in O. V. Komogortsev, et al., Standardization of Automated Analyses of Oculomotor Fixation and Saccadic Behaviors, IEEE Transactions on Biomedical Engineering 57, 11 (2010), 2635, which is incorporated by reference as if fully set forth herein. In general, the values of the behavioral score may signal a) meaningfulness of eye movement classification, b) eye tracking quality, c) “normality” of the captured eye movement signal. By controlling for a) and b), the ability to detect ab-normality of the captured eye movement signal may be used to assess and detect fatigue.

Fixations and saccades may represent the majority of movements during a typical HCI related task, such as pointing, typing, reading, menu selection, etc.

Fixation Quantitative Score (FQnS): An FQnS may represent the amount of fixational behavior in the recorded data. The FQnS may take into the account the latency associated with user reaction in response to the stimulus and considers fixations only in the meaningful proximity to the presented stimulus. The FQnS may be computed by the equation:

${FQnS} = {100 \cdot {\frac{{fixation\_ detection}{\_ counter}}{{stimuli\_ fixation}{\_ points}}.}}$

The FQnS may be representative of both number of exhibited fixations and their durations and has less variability that either of those metrics.

Fixation Qualitative Score (FQlS): An FQlS may indicates the spatial accuracy of the exhibited fixations in response to the presented stimulus. The FQlS may be computed by the equation:

${FQlS} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\; {{fixation\_ distance}_{i}.}}}$

A fatigued user may frequently miss the target on the initial fixation and therefore some of the fixations may be located further away from the target than during normal “rested” baseline. In some embodiments, FQlS increases when a user becomes fatigued.

Saccade Quantitative Score (SQnS): An SQnS may represent the amount of saccadic behavior captured in response to the presented stimuli. The SQnS may sum the amplitude of all captured saccades and divides them by the sum of saccade amplitudes encoded in the stimulus. SQnS may be computed by the equation:

${SQnS} = {100 \cdot {\frac{{total\_ deteced}{\_ saccade}{\_ amplitude}}{{total\_ stimuli}{\_ saccade}{\_ amplitude}}.}}$

SQnS change during the fatigue may in some cases be similar to the ones provided in for the FQlS. When a user misses the target on the initial saccadic movement the miss may be subsequently corrected with an additional saccade. These additional corrective saccades in cases when the eye overshoots or undershoots a target lead to the larger amount of saccadic behavior when compared to the baseline. An increase in SQnS may be detected when a user becomes fatigued.

In addition to the behavior scores, any or all of the following characteristics may be considered in fatigue detection in various embodiments: Average Fixation Duration (AFD), Average Number of Fixations (ANF), Average Saccade Duration (ASD), Average Number of Saccades (ANS), Average Saccade Amplitude (ASA), Average Saccade Duration (ASD), and Average Peak Saccade Velocity (APSV).

Experiment

Recording Equipment

The data was recorded using the EyeLink 1000 eye tracker with a sampling frequency of 1000 Hz. The Eye-Link 1000 provides drift free eye tracking with a spatial resolution of 0.01°, and 0.25-0.5° of positional accuracy. The recording was conducted on a tower mount with chinrest to improve the accuracy and reduce the noise in the recorded data. The stimulus was presented on 22 inch flat panel wide-screen LCD display with refresh rate of 60 Hz. The display measured 474×297 millimeters and resolution was 1680×1050 pixels. The chinrest was located 550 mm from the display. For each subject the chinrest was adjusted to ensure that primary eye position (eye is staring straight ahead) would correspond to the center of the screen.

Recording Procedure & Stimulus

The horizontal step stimulus was displayed as a jumping dot, consisting of a grey disc sized approximately 1° with a small black point in the center. The dot performed 100 jumps horizontally, each time the dot was stationary for is before the next jump. The jumps had the amplitude of 30°, which corresponded to the dot location +15° and −15° from the center of the display. The very first dot location was display's center. Each participant was instructed to follow the dot movements. This stimulus was selected to be simpler than normal calibration routine, longer, and more repetitive to monitor possible development of the fatigue effects.

The presentation of this stimulus was a part of a larger ocular biometrics study that consisted of two recording sessions conducted 10 minutes apart with the total duration of the experiment not exceeding 1 hour. Each session consisted of pure fixational stimulus, horizontal stimulus (analyzed here), movie trailer part 1, random saccades, text reading, gaze-controlled computer, game, movie trailer part 2. Participants filled several questionnaires prior recordings, between two sessions, and after all recordings.

This recording procedure allowed monitoring fatigue onset within a single repetitive task, such as horizontal saccades stimulus and also monitor fatigue related changes between sessions, assuming that during session two (S2) participants become generally more fatigued than during session one (S1).

Participants, Eye Movement Classification & Quality of the Recorded Data

A total of 25 participants (15 males/10 females), ages 18-33 years with an average age of 22.3 (SD=3.8), volunteered for the project. Only two participants had contact lenses on, all remaining participants had self-reported normal quality of vision and did not wear any corrective eyewear. Verified mean positional accuracy of the recordings averaged between all screen regions was 1.41° (SD=1.91°). Average recorded data validity was 96.08% (SD=5.20%). Collected eye movement data was classified by the I-VT algorithm [8] with the separation threshold of 70°/s. Both positional accuracy and data validity numbers indicate that captured data quality was high. Eye movement classification via the I-VT algorithm and high quality of the recorded data indicate that that the changes in behavior scores would represent the change in the eye movement behavior, e.g., fatigue onset, rather than failures of eye movement classification or/and recording equipment. After fixations and saccades were classified with I-VT all classified saccades were manually examined to ensure that they represent actual velocity profiles. Saccades that contained blinks were removed from the final analysis. Blinks were detected by a mechanism specified by Bahill and Kallman. To compute APSV metric the velocity during each saccade was calculated in order to reduce the impact of noise and variability present in the signal.

Data Partitioning & Analysis Methods

The recordings for each session were broken into 10 groups with 10 stimulus dot jumps per group and corresponding recorded eye movement signal. In reported results each group is marked as 1G, 2G, . . . , 10G and recording sessions are abbreviated as S1 and S2.

General Linear Model Repeated Measures ANOVA was employed because each participant was recorded for all factors levels, i.e., partition groups in this case. The approach was employed to find statistically significant effects among scores computed between groups in each session and for the averaged scores between each whole session. Separately, statistical effects were looked for between first (1G) group, i.e., normal baseline, and last (10G) group, i.e., fatigued baseline, in each session with an assumption that the difference in scores would most substantial in those cases. For results that involve the comparison between all 10 groups Bonferroni correction was performed, which reduced statistically significant level to 0.005 instead of 0.05. In this experiment, all factors were within-subject, and between-subject factors were not assessed. (Nevertheless, in some embodiments, between-subject factors may be assessed, instead of, or in addition to, within-subject factors.)

Results

FIGS. 23-30 present the results of the experiment. Most metrics are represented by bar graphs with exception of those scores that produced statistically significant results between all groups within a session, in which case linear regression model is presented to show the trend.

Fixation Quantitative Score (FQnS): FIG. 23 is a bar graph representing fixation quantitative scores for the sessions. No statistical differences were observed between scores and scores remained almost at the same level, indicating that corresponding fixational behavior was registered in response to the stimulus.

Fixation Qualitative Score (FQlS): FIG. 24 show regression for fixation qualitative scores for a session. The score has steadily increased as task progressed, which is one of the indicators of the increased corrective eye movement behavior, i.e., overshoots or/and undershoots. Statistically significant results were obtained between all groups in each session, i.e., S1: F(1,24)=6.1, p<0.001, S2: F(1,24)=17.2, p<0.001.

Saccade Quantitative Score (SQnS): FIG. 25 is a bar graph representing saccade quantitative scores for the sessions. The score increased in both sessions when the score for G1 was compared to G10, indicating that the amount of saccadic behavior increased signaling the increase in corrective behavior associated with the onset of fatigue. Both differences were statistically significant, i.e., S1: F(1,24)=13.9, p<0.01, S2: F(1,24)=7.1, p<0.05.

Average Fixation Duration (AFD): FIG. 26 shows regression for average fixation duration for a session. The score has steadily degreased signaling that the detected fixations became shorter, which is also indicative of corrective behavior signaling fatigue. Therefore we can confirm the result mentioned in previous work. The result was statistically significant between all groups only in S1 with corresponding F(1,24)=29.1, p<0.001. Between G1 and G10 the difference was statistically significant also only during first session, i.e., S1: F(1,24)=29.090, p<0.001. AFD is the only metric that produced statistically significant results when the score was averaged in each session and compared, F(1,24)=6.166, p<0.05.

FIG. 27 is a bar graph representing average number of saccades (ANS) for the session. Among remaining scores the ANS and ANF increased, again indicating the presence of corrective behavior. The results were statistically significant only during first session.

FIG. 28 is a bar graph representing average saccade amplitude for the sessions. FIG. 29 is a bar graph representing average saccade duration for the sessions.

APSV: FIG. 30 is a bar graph showing average saccade peak velocity for the sessions. APSV decreased when G1 was compared to G2, which confirms previously published result. The result was statistically significant only during S1: F(1,24)=8.417, p<0.01. The reduction in APSV can be partially explained by smaller saccades, which again signals corrective behavior, which we assume, appears in case of the fatigue. The behavior during S2 where the subject was potentially already fatigued is more complex and will be investigated in our future work.

In many embodiments described herein, a method of system is stated as detecting autism and/or fatigue. It will be understand the methods and systems may, in various embodiments, be used to detect only fatigue, or to detect only autism.

In various embodiments, systems and methods described herein are used print-attack detection, pre-recorded attack detection, or both, based on measured eye movement. In certain embodiments, a system and method as described herein detects the presence of contact lens based on measured eye movement. In certain embodiments, a system and method as described herein detects a mechanical replica based on measured eye movement.

Further Improvements

In various embodiments, eye-movements from categories including fixations, saccades, and glissades, are used in biometrics and health assessment. Illustrative features are described below. The features described herein include a variety of heterogeneous features related both to the physiological properties of the oculomotor system, and to the cognitive and behavioral characteristics of different subjects. Presented features may greatly improve eye movement-driven biometrics performance and also improve the accuracy of biometrics-driven health assessment. Health assessment includes, but is not limited to the detection of such states as concussion, fatigue, autism, Parkinson's disease, dementia, schizophrenia, bipolar disorder, virtual reality sickness, and any other state that manifest itself by alteration of the performance of the human visual system. In various embodiments, temporal, positional and dynamic properties of fixations, saccades, and glissades are assessed. In certain embodiments, features are based on the formulas for the computation of these features by using directly the position, velocity and acceleration eye-tracking signals as described below. Each of the features described below may be used in assessments as described in the systems and processes described with respect to FIGS. 1-30.

Methods and systems for assessing a health state of a person via eye movement-driven biometric systems are provided. Examples of the health states that it would be possible to detect with such a system are but not limited to brain injuries (e.g., concussions), dementia, Parkinson's disease, post-traumatic stress syndrome, schizophrenia, fatigue, autism, Bipolar Disorder and other health conditions that manifest themselves in abnormal behavior of the human visual system. Described methods and systems can also detect influence of alcohol and/or drugs. In one embodiment, a systems using eye movement to detect cybersickness. In various embodiments, the system extracts biometric template of a person by deriving features from the captured eye movement signal. The system may compare the difference between previous healthy state of a tested person and newly captured template or an averaged biometric template created from the records of multiple healthy people state of multiple people and a newly captured template from a person who needs to be tested. Based on the difference between the templates, a decision of a health state of a person is made. Described methods and systems may work on any device that has eye tracking capabilities including but not limited to desktop mounted eye tracking systems, head mounted eye tracking systems such as Virtual Reality and Augmented Reality or stand-alone mounted eye tracking systems.

In some embodiments, a system that measures eye movement (such as, for example, the systems described relative to FIGS. 1-30) includes virtual reality or augmented reality devices. Eye movement measurements may be used to assess health or other conditions of the subject experiencing virtual reality or augmented reality. For example, a person's level or fatigue, or whether they are suffering, whether the subject is intoxicated, or whether the subject has suffered traumatic brain injury may be determined based on eye movement ins response to stimuli presents in a virtual reality or augmented reality environment.

In certain embodiments, eye movement data and/or subject assessment data is used by a system to determine the content to be presented to the user of the virtual reality or augmented reality system. For example, if the system determines from eye movement data that a subject is distressed or frightened during a virtual reality experience, the system may alter the programming to present imagery to relax the subject.

Extraction of eye movement features

Preprocessing

Prior to the extraction of eye movement features the raw eye movement recordings may be preprocessed with the use of a robust eye movement classification algorithm w based on an adaptive velocity threshold classification methodology. The algorithm classifies three main types of eye movement events: fixations, saccades, and glissades. The classification accuracy of the employed algorithm was verified via visual inspection of the classified eye movement events by four trained expert subjects.

General Overview

For each kind of eye movement event (fixation/saccade/glissade), a number of features in are extracted order to describe its temporal, frequency-of-occurrence, shape, and dynamic characteristics. In one embodiment, there are two categories of features, ‘distributional’ and ‘non-distributional’. For each ‘distributional’ feature the values are extracted from every classified instance of the event under consideration (fixation, saccade, or glissade). Given the fact that more than one instances of an event occur in a recording, the corresponding feature-values form a distribution. Six different descriptive statistics are calculated over the distribution of the feature values from all instances in a recording (mean-MN, median-MD, standard deviation-SD, interquartile range-IQ, skewness-SK, and kurtosis-KU), thus deriving six values for each specific ‘distributional’ feature. It should be noted that for the case of saccades/glissades the values are post-filtered before the calculation of statistics (values corresponding to saccades with amplitude>8° and duration>70 ms may be excluded). On the other hand, for the ‘non-distributional’ features a single feature value is directly calculated by collectively modeling all the instances of an event (fixation/saccade/glissade). Table VI provides some useful notations that will facilitate the understanding of the feature descriptions in the following sections.

TABLE IV Symbols and notation i: index used to refer at each single fixation/saccade/glissade of a recording j: index used to refer at the samples within each single fixation/saccade/glissade of a recording N: general notation used to represent the total number of samples within a fixation/saccade/glissade. Usually, this number would be different for each single fixation/saccade/glissade Fix^(Num), Sac^(Num), Gls^(Num): number of fixations/saccades/glissades of a recording DistrStat(•): this notation is used as superscript to denote ‘distributional’ features, i.e. features that are calculated via the application of descriptive statistics on a basic feature type x extracted from each fixation/saccade/glissade of a recording. The used descriptive statistics are DistrDistrStat(x) = mean, median, standard deviation, interquartile range, skewness, and kurtosis HVR (or HV or R): This notation is used as superscript to denote features that are calculated separately for the horizontal, vertical, and radial profiles* of eye movement (or horizontal-vertical profiles only or radial profile only). A feature with a superscript DistrStat-HVR means both that it is a ‘distributional’ feature and that it is extracted for the three components separately (6 × 3 = 18 values) HV2D: This notation is used as superscript to denote features that are calculated using the values of eye movement position samples in the 2-D plane (2D-trajectory) FixPos_(i)(j), SacPos_(i)(j), GlsPos_(i)(j): This notation is used to denote the j^(th) positional sample (or equally the j^(th) sample of the position profile) of the i^(th) fixation/saccade/glissade FixVel_(i)(j), SacVel_(i)(j), GlsVel_(i)(j): This notation is used to denote the j^(th) velocity sample (or equally the j^(th) sample of the velocity profile) of the i^(th) fixation/saccade/glissade FixAcc_(i)(j), SacAcc_(i)(j), GlsAcc_(i)(j): This notation is used to denote the j^(th) acceleration sample (or equally the j^(th) sample of the acceleration profile) of the i^(th) fixation/saccade/glissade *The term profile (position, velocity, acceleration) refers to the variation of a quantity as a function of time/samples

Fixation Features

The term fixation is used to define the state when the eyes are focused at a specific point of interest, bringing the content of this area at the central high-resolution region of the eye retina (fovea centralis). During the state of fixation the eyes perform a variety of miniature movements such as: slow ocular drifts, small-amplitude saccades (micro-saccades), and high-frequency tremors (known as physiological nystagmus) [13]. The fixations performed during the task of reading can present a large variety of temporal, positional, and dynamic characteristics. For this reason, we have grouped these features according to their general properties and we present them below.

Features of Fixation Temporal and Frequency-of-Occurrence Characteristics

The temporal characteristics of the fixations performed during reading of a text passage can provide valuable information about the reading behavior and the cognitive characteristics of the person performing the task. The following features were extracted to describe the temporal characteristics of fixations:

TABLE V Fixation temporal and frequency-of-occurrence features F01: Fix_(Rate) The fixation rate: Fix^(Num)/Rec^(dur) F02: FixDur^(DistrStat) DistrStat(•) over the durations of fixations: FixDur_(i), i = 1, . . . , Fix^(Num)

Features of Fixation Drift

The fixation drifts are manifested as slow movements of the eye away from a fixated location. The exact path followed during a drift can reveal both subject-specific properties and device-specific characteristics, making the modeling of drifts valuable both in the examination of user ocular behavior and in the evaluation of a human computer interaction setup. FIG. 31 includes some examples of fixation drifts showing their basic characteristics. Due to the existing diversity in fixation drift manifestation, there is a variety of features that can be extracted to model their characteristics:

TABLE VI Fixation drift features F03: FixDriftDisp^(DistrStat-HVR) DistrStat(•) over the drift displacements of fixations: FixDriftDisp_(i) = |FixPos_(i)(end) − FixPos_(i)(start)|,i = 1, . . . , Fix^(Num) F04: FixDriftDist^(DistrStat-HVR) DistrStat(•) over the drift distances of fixations: FixDriftDist_(i) = Σ_(j=1) ^(N−1)|FixPos_(i)(j + 1) − FixPos_(i)(j)|, i = 1, . . . , Fix^(Num) F05: FixDriftAvgSpeed^(DistrStat-HVR) DistrStat(•) over the drift average speeds of fixations: FixDriftAvgSpeed_(i) = FixDriftDisp_(i)/FixDur_(i), i = 1, . . . , Fix^(Num) F06: FixDriftFitLn_(Slope) ^(DistrStat-HV) DistrStat(•) over the slope of linear regression fit on drift of fixations: FixDriftFitLn_(Slope) _(i) calculated from the linear regression fit on all positional samples FixPos_(i)(j), j = 1, . . . , N within each fixation i, i = 1, . . . , Fix^(Num) F07: FixDriftFitLn_(R) ₂ ^(DistrStat-HV) DistrStat(•) over the R² of linear regression fit on drift of fixations: FixDriftFitLn_(R) ₂ _(i) calculated from the linear regression fit on all positional samples FixPos_(i)(j), j = 1, . . . , N within each fixation i, i = 1, . . . , Fix^(Num) F08: FixDriftFitQd_(R) ₂ ^(DisrStat-HV) DistrStat(•) over R² of the quadratic regression fit on drift of fixations: FixDriftFitQd_(R) ₂ _(i) calculated from the quadratic regression fit on all positional samples FixPos_(i)(j), j = 1, . . . , N within each fixation i, i = 1, . . . , Fix^(Num) F09: FixDriftPrL0Q0^(HV) The L0Q0 parameter percentage: 100% · Σ_(i=1) ^(Fix) ^(Num) L0Q0_(i)/Fix^(Num), with the L0Q0_(i) calculated from the stepwise regression fit on all positional samples FixPos_(i)(j), j = 1, . . . , N within each fixation i, i = 1, . . . , Fix^(Num) F10: FixDriftPrL0Q1^(HV) The L0Q1 parameter percentage: 100% · Σ_(i=1) ^(Fix) ^(Num) L0Q1_(i)/Fix^(Num), with the L0Q1_(i) calculated from the stepwise regression fit on all positional samples FixPos_(i)(j), j = 1, . . . , N within each fixation i, i = 1, . . . , Fix^(Num) F11: FixDriftPrL1Q0^(HV) The L1Q0 parameter percentage: 100% · Σ_(i=1) ^(Fix) ^(Num) L1Q0_(i)/Fix^(Num), with the L1Q0_(i) calculated from the stepwise regression fit on all positional samples FixPos_(i)(j), j = 1, . . . , N within each fixation i, i = 1, . . . , Fix^(Num) F12: FixDriftPrL1Q1^(HV) The L1Q1 parameter percentage: 100% · Σ_(i=1) ^(Fix) ^(Num) L1Q1_(i)/Fix^(Num), with the L1Q1_(i) calculated from the stepwise regression fit on all positional samples FixPos_(i)(j), j = 1, . . . , N within each fixation i, i = 1, . . . , Fix^(Num)

Features of Fixation Position

The position characteristics of fixations may be considered with caution due to the fact that they can be highly affected by the exact stimulus layout. In cases when the same stimulus is used, though, they can be used in order to provide information about the gaze error induced by subject-specific or device-specific reasons. A basic feature that can be extracted to model the fixation position characteristics is the fixation centroid:

TABLE VII Fixation position features F13: FixPosCentroid^(DistrStat-HV) DistrStat(•) over the position centroids of fixations: FixPosCentroid_(i) = Σ_(j=1) ^(N) FixPos_(i)(j)/N, i = 1, . . . , Fix^(Num)

Features of Fixation Velocity and Velocity Noise

In contrast to the case of saccades (see next session) (where the velocity and acceleration profiles can potentially reflect rich information about the mechanics and the neural substrate guiding eye movements) in the case of fixations the velocity (and acceleration) profiles reflect mostly the properties of signal noise. FIG. 32 presents examples of and acceleration profiles (horizontal-vertical) of a fixation. Although the effects of noise are evident, some characteristics of the velocity profiles can also reflect information potentially related to micro-movements (e.g. tremors etc.) and other abnormalities. Among the extracted features, there is a special category of features that are extracted to model the general shape properties of a velocity profiles by applying five descriptive statistics on the values within each profile (mean, median, standard deviation, skewness and kurtosis). This should not be confused with the application of descriptive statistics to model the feature values from all instances (values' distribution) which happens at a later stage.

TABLE VIII Fixation velocity features F14: FixVelProfMn^(DistrStat−HVR) DistrStat(·) over the mean of velocity profiles of fixations: FixVelProfMn_(i) = Σ_(j=1) ^(N)|FixVel_(i)(j)|/N, i = 1, . . . , Fix^(Num) F15: FixVelProfMd^(DistrStat−HVR) DistrStat(·) over the median of velocity profiles of fixations: FixVelProfMd_(i) = median(|FixVel_(i)|), i = 1, . . . , Fix^(Num) F16: FixVelProfSd^(DistrStat−HVR) DistrStat(·) over the standard deviation of velocity profiles of fixations: FixVelProfSd_(i) = {square root over (Σ_(j=1) ^(N)(|FixVel_(i)(j)| − FixVelProfMn_(i))²/N)}, i = 1, . . . , Fix^(Num) F17: FixVelProfSk^(DistrStat−HVR) DistrStat(·) over the skewness of velocity profiles of fixations: FixVelProfSk_(i) = $\frac{\sum_{j = 1}^{N}{\left( {{{{FixVel}_{i}(j)}} - {FixVelProfMn}_{i}} \right)^{3}\text{/}N}}{\left( \sqrt{\sum_{j = 1}^{N}{\left( {{{{FixVel}_{i}(j)}} - {FixVelProfMn}_{i}} \right)^{2}\text{/}N}} \right)^{3}},{i = 1},\ldots \mspace{14mu},{Fix}^{Num}$ F18: FixVelProfKu^(DistrStat−HVR) DistrStat(·) over the kurtosis of velocity profiles of fixations: FixVelProfKu_(i) = $\frac{\sum_{j = 1}^{N}{\left( {{{{FixVel}_{i}(j)}} - {FixVelProfMn}_{i}} \right)^{4}\text{/}N}}{\left( {\sum_{j = 1}^{N}{\left( {{{{FixVel}_{i}(j)}} - {FixVelProfMn}_{i}} \right)^{2}\text{/}N}} \right)^{2}},{i = 1},\ldots \mspace{14mu},{Fix}^{Num}$ F19: FixVelNoiseMode^(R) Mode value caculated over a vector that contains collectively the velocity samples from the central parts of all fixations FixVel_(i)(j), j → samples starting after and finishing before a time parameter t_(d) = 5 ms, i = 1, . . . , Fix^(Num) F20: FixVelNoiseP90^(R) 90^(th) Percentile value calculated over a vector that contains collectively the velocity samples from the central parts of all fixations FixVel_(i)(j), j → samples starting after and finishing before a time parameter t_(d) = 5 ms, i = 1, . . . , Fix^(Num) F21: FixVelNoiseIqr^(R) Interquartile range value calculated over a vector that contains collectively the velocity samples from the central parts of all fixations FixVel_(i)(j), j → samples starting after and finishing before a time parameter t_(d) = 5 ms, i = 1, . . . , Fix^(Num) F22: FixPrAbNoiseP90^(DistrStat−R) DistrStat(·) over the percentages of the velocity samples of fixations that are above FixVelNoiseP90 value: FixPrAbNoiseP90_(i), i = 1, . . . , Fix^(Num) F23: FixPrCrNoiseP90^(DistrStat−R) DistrStat(·) over the percentages of the velocity samples of fixations crossing FixVelNoiseP90 value: FixPrCrNoiseP90_(i), i = 1, . . . , Fix^(Num)

Features of Fixation Acceleration

The effects of noise are even more pronounced in the case of the acceleration profiles. However, the general characteristics of the profiles can be studied as sources of complementary information about the eye dynamics during fixations. As previously, the basic category of features involves the application of descriptive statistics to model the characteristics of the acceleration profiles.

TABLE IX Fixation acceleration features F24: FixAccProfMn^(DistrStat−HVR) DistrStat(·) over the mean of acceleration profiles of fixations: FixAccProfMn_(i) = Σ_(j=1) ^(N)|FixAcc_(i)(j)|/N, i = 1, . . . , Fix^(Num) F25: FixAccProfMd^(DistrStat−HVR) DistrStat(·) over the median of acceleration profiles of fixations: FixAccProfMd_(i) = median(|FixAcc_(i)|), i = 1, . . . , Fix^(Num) F26: FixAccProfSd^(DistrStat−HVR) DistrStat(·) over the standard deviation of acceleration profiles of fixations: FixAccProfSd_(i) = {square root over (Σ_(j=1) ^(N)(|FixAcc_(i)(j)| − FixAccProfMn_(i))²/N)}, i = 1, . . . , Fix^(Num) F27: FixAccProfSk^(DistrStat−HVR) DistrStat(·) over the skewness of acceleration profiles of fixations: FixAccProfSk_(i) = $\frac{\sum_{j = 1}^{N}{\left( {{{{FixAcc}_{i}(j)}} - {FixAccProfMn}_{i}} \right)^{3}\text{/}N}}{\left( \sqrt{\sum_{j = 1}^{N}{\left( {{{{FixAcc}_{i}(j)}} - {FixAccProfMn}_{i}} \right)^{2}\text{/}N}} \right)^{3}},{i = 1},\ldots \mspace{14mu},{Fix}^{Num}$ F28: FixAccProfKu^(DistrStat−HVR) DistrStat(·) over the kurtosis of acceleration profiles of fixations: FixAccProfKu_(i) = $\frac{\sum_{j = 1}^{N}{\left( {{{{FixAcc}_{i}(j)}} - {FixAccProfMn}_{i}} \right)^{4}\text{/}N}}{\left( {\sum_{j = 1}^{N}{\left( {{{{FixAcc}_{i}(j)}} - {FixAccProfMn}_{i}} \right)^{2}\text{/}N}} \right)^{2}},{i = 1},\ldots \mspace{14mu},{Fix}^{Num}$

Saccade Features

Saccades are very fast movements (peak velocities up to 900°/s) that transfer the eyes from one position of fixation to another. Before the initiation of a saccade, the saccade-generating neural circuitry calculates the difference from the starting position to the landing position and sends the guiding neural pulses to the extraocular muscles that rotate the eye. Saccades are ballistic movements, when a saccade has been initiated the eyes cannot change their trajectory ‘en route’ even if the original target changes position. When the eyes miss the intended position one or more small corrective saccades can be implemented to transfer eyes to the final position.

Features of Saccade Amplitude

One of the basic characteristics of saccades is their amplitude. The values of amplitude can provide useful information especially when they are considered in relation to other saccadic characteristics like the duration and the peak velocity. The basic feature that can be extracted to describe the saccade amplitude is the following:

TABLE X Saccade amplitude features S01: SacAmp^(DistrStat-HVR) DistrStat(•) over the amplitudes of saccades: SacAmp_(i) = |SacPos_(i)(end) − SacPos_(i)(start)|, i = 1, . . . , Sac^(Num)

Features of Saccade Temporal and Frequency-of-Occurrence Characteristics

As in the case of fixations, we can extract two features for describing the basic temporal characteristics of the performed saccades, the rate and the duration.

TABLE XI Saccade temporal features S02: Sac_(Rate) The saccade rate: Sac^(Num)/Rec^(dur) S03: SacDur^(DistrStat) DistrStat(•) over the durations of saccades: SacDur_(i), i = 1, . . . , Sac^(Num)

Features of Saccade Shape and Curvature

FIG. 33 presents examples showing the shape (amplitude and curvature) of saccades both in the time domain and in the 2D-plane. It can be clearly observed that the trajectory of saccades is not linear but it presents a curvature. The saccade amplitude is only a limited representation of the more complex characteristics appearing in saccades' trajectories. A large number of features for modeling saccade curvature were presented by Ludwig and Gilchrist. Certain of these features are extracted below along with some additional new features that model the ending parts (tails) of saccades, due to the experimentally observed diversity in shapes of these parts.

TABLE XII Saccade shape and curvature features S04: SacTravDist^(DistrStat−R) DistrStat(·) over the travelled distances of saccades: SacTravDist_(i) = Σ_(j=1) ^(N−1)|SacPos_(i)(j + 1) − SacPos_(i)(j)|, i = 1, . . . , Sac^(Num) S05: SacEfficiency^(DistrStat−R) ${{{{DistrStat}( \cdot )}\mspace{14mu} {over}\mspace{14mu} {the}\mspace{14mu} {efficiency}\mspace{14mu} {metrics}\mspace{14mu} {of}\mspace{14mu} {saccades}\text{:}\mspace{11mu} {SacEfficiency}_{i}} = \frac{{SacAmp}_{i}}{{SacTravDist}_{i}}},{i = 1},\ldots \mspace{14mu},{Sac}^{Num}$ S06: SacTailEfficiency^(DistrStat−R) DistrStat(·) over the tail efficiency metrics of saccades: SacTailEfficiency_(i) = $\frac{{SacTailAmp}_{i}}{{SacTailTravDist}_{i}}, {i = {\quad{1,\ldots \mspace{14mu},{Sac}^{Num},{{where}\mspace{14mu} {SacTailAmp}_{i}},{{SacTailTravDists}_{i}\mspace{14mu} {are}\mspace{14mu} {the}}}}}$ amplitudes and travelled distances calculated from the samples of the last 7 ms of a saccade S07: SacTailPrInconsist^(DistrStat−HV2D) DistrStat(·) over the percentage tail inconsistency metrics of saccades: SacTailPrInconsist_(i), i = 1, . . . , Sac^(Num) is the percentage of samples of the last 7 ms of a saccade for which angle(LocalDir_(i), OverDir_(i)) ≧ 60° ., where LocalDir_(i) is the vector connecting the current and the previous point of a saccade, and OverDir_(i) the vector connecting the starting and the ending point of a saccade (for this feature raw signal was used) S08: SacInitDir^(DistrStat−HV2D) DistrStat(·) over the initial direction curvature metrics of saccades: SacInitDir_(i) = angle(InitDir_(i), OverDir_(i)), i = 1, . . . , Sac^(Num), where InitDir_(i) is the vector connecting the starting point of a saccade to a predefined point (20 ms after the starting point), and OverDir_(i) the vector connecting the starting to the ending point of a saccade (in x-y plane) S09: SacInitAvgDev^(DistrStat−HV2D) DistrStat(·) over the initial average deviation curvature metrics of saccades: SacInitAvgDev_(i) = Σ_(j=1) ^(m) InitDev_(i)(j), i = 1, . . . , Sac^(Num), where m samples in a predefined window of 10 ms after the starting point of a saccade. InitDev_(i)(j) is calculated by subtracting the eye position (of sample j) on the dimension orthogonal to the saccade direction (e. g., horizontal for vertical saccades) from the value on that dimension at the start of the saccade (e. g., horizontal starting position). S10: SacMaxRawDev^(DistrStat−HV2D) DistrStat(·) over the maximum raw deviation curvature metrics of saccades: SacMaxCurv_(i) = max_(j=1, . . . , N)|PerpDist(j)|, i = 1, . . . , Sac^(Num), where PerpDist(j) is the perpendicular distance (deviation) of a sample j from a straight line between the starting point and the ending point of a saccade S11: SacPoiMaxRawDev^(DistrStat−HV2D) DistrStat(·) over the points of maximum raw deviation curvature of saccades (calculated as described above), where each point is expressed as percentage of the total duration of a saccade S12: SacAreaCurv^(DistrStat−HV2D) DistrStat(·) over the area curvature metrics of saccades: SacAreaCurv_(i) = Σ_(j=2) ^(N)StrDist(j) · PerpDist(j), i = 1, . . . , Sac^(Num), where StrDist(j) is the distance covered by sample j along the straight path between onset and endpoint since the previous sample (j-1), and PerpDist(j) is the perpendicular (signed) deviation of sample j S13: SacQuadCurv^(DistrStat−HV2D) DistrStat(·) over the quadratic-fit curvature metrics of saccades: SacQuadCurv_(i) i = 1, . . . , Sac^(Num) is the quadratic coefficient calculated from the fitting of a quadratic function on the position points of a saccade. Every saccade is translated so that the axis through its starting and ending positions coincides with the abscissa. The horizontal axis is rescaled so that each saccade starts at −1 and ends at +1 S14: SacCubCurvM1^(DistrStat−HV2D) DistrStat(·) over the cubic-fit-first-maximum curvature metrics of saccades: SacCubCurvM1_(i) i = 1, . . . , Sac^(Num) is the maximum of the cubic function fitted on the position points of a saccade (under the same transformations as above) S15: SacPoiCubCurvM1^(DistrStat−HV2D) DistrStat(·) over the points of cubic-fit-first-maximum curvature of saccades (calculated as described above), where each point is expressed as percentage of the total duration of a saccade S16: SacCubCurvM2^(DistrStat−HV2D) DistrStat(·)over the cubic-fit-second-maximum curvature metrics of saccades: SacCubCurvM2_(i) i = 1, . . . , Sac^(Num) is the minimum of the cubic function fitted on the position points of a saccade (under the same transformations as above). S17: SacPoiCubCurvM2^(DistrStat−HV2D) DistrStat(·) over the points of cubic-fit-second-maximum curvature of saccades (calculated as described above), where each point is expressed as percentage of the total duration of a saccade S18: SacCubCurvMax^(DistrStat−HV2D) DistrStat(·) over the cubic-fit-overall-maximum curvature metrics of saccades: SacCubCurvMax_(i) = max(SacCubCurvM1_(i), SacCubCurvM2_(i)), i = 1, . . . , Sac^(Num) S19: SacPoiCubCurvMax^(DistrStat−HV2D) DistrStat(·) over the points of cubic-fit-overall-maximum curvature of saccades (calculated as described above), where each point is expressed as percentage of the total duration of a saccade

Features of Saccade Velocity

The features that describe the dynamics of saccades are particularly important since they can reflect various properties of the underlying oculomotor mechanisms involved in the generation of eye movement. FIG. 34 characteristic velocity profiles (horizontal/vertical) for saccades performed during reading. In contrast to fixations where velocity profiles are strongly affected by noise, the velocity profiles of saccades present characteristic bell-like shapes representing the increase of velocity until reaching a peak and then the decrease until the eye reaches its final position. The following features are extracted to model the characteristics of the velocity profiles of saccades:

TABLE XIII Saccade velocity features S20: SacNumVelLocMin^(DistrStat−R) DistrStat(·) over the number of local minima in velocity of saccades: SacNumVelLocMin_(i) is the number of sign changes from negatvie to positive in vector SignVel(j) = sign(SacVel_(i)(j) − SacVel_(i)(j − 1)), j = 2, . . . , N, i = 1, . . . , Sac^(Num) S21: SacPkVel^(DistrStat−HVR) DistrStat(·) over the peak velocities of saccades: SacPkVel_(i) = max_(j=1, . . . , N)|SacVel_(i)(j)|, i = 1, . . . , Sac^(Num) S22: SacVelProfMn^(DistrStat−HVR) DistrStat(·) over the mean of velocity profiles of saccades: SacVelProfMn_(i) = Σ_(j=1) ^(N)|SacVel_(i)(j)|/N, i = 1, . . . , Sac^(Num) S23: SacVelProfMd^(DistrStat−HVR) DistrStat(·) over the median of velocity profiles of saccades: SacVelProfMd_(i) = median(|SacVel_(i)|), i = 1, . . . , Sac^(Num) S24: SacVelProfSd^(DistrStat−HVR) DistrStat(·) over the standard deviation of velocity profiles of saccades: SacVelProfSd_(i) = {square root over (Σ_(j=1) ^(N)(|SacVel_(i)(j)| − SacVelProfMn_(i))²/N)}, i = 1, . . . , Sac^(Num) S25: SacVelProfSk^(DistrStat−HVR) DistrStat(·) over the skewness of velocity profiles of saccades: SacVelProfSki = $\frac{\sum_{j = 1}^{N}{\left( {{{{SacVel}_{i}(j)}} - {SacVelProfMn}_{i}} \right)^{3}\text{/}N}}{\left( \sqrt{\sum_{j = 1}^{N}{\left( {{{{SacVel}_{i}(j)}} - {SacVelProfMn}_{i}} \right)^{2}\text{/}N}} \right)^{3}},{i = 1},\ldots \mspace{14mu},{Sac}^{Num}$ S26: SacVelProfKu^(DistrStat−HVR) DistrStat(·) over the kurtosis of velocity profiles of saccades: SacVelProfKu_(i) = $\frac{\sum_{j = 1}^{N}{\left( {{{{SacVel}_{i}(j)}} - {SacVelProfMn}_{i}} \right)^{4}\text{/}N}}{\left( {\sum_{j = 1}^{N}{\left( {{{{SacVel}_{i}(j)}} - {SacVelProfMn}_{i}} \right)^{2}\text{/}N}} \right)^{2}},{i = 1},\ldots \mspace{14mu},{Sac}^{Num}$ S27: SacPrePk_(Pr) ^(R) The percentage of saccades with a pre-peak in their velocity profile. The pre-peaks are identified as small peaks occurring before the main peak in velocity profile

Features of Saccade Acceleration

Due to the direct relation of the acceleration with the forces applied on the eye during the eye movement, the acceleration profiles present particular research interest. Previous studies have showed the existence of asymmetries in the shapes of acceleration profiles (acceleration-deceleration phases) and the modulation of their characteristics by motor learning. FIG. 34 shows examples of acceleration profiles (horizontal/vertical) for a saccade. Some basic characteristics like the differences in the peak values and durations of the acceleration and deceleration phases. The following features are extracted to describe the properties of acceleration of saccades:

TABLE XIV Saccade acceleration features S28: SacPkAcc^(DistrStat−HVR) DistrStat(·) over the peak accelerations of saccades: SacPkAcc_(i) = max_(j=1, . . . , idx−1)|SacAcc_(i)(j)|, i = 1, . . . , Sac^(Num), where idx is the sample where SacPkVel_(i) occurs S29: SacPkDec^(DistrStat−HVR) DistrStat(·) over the peak decelerations of saccades: SacPkDec_(i) = max_(j=idx+1, . . . , N)|SacAcc_(i)(j)|, i = 1, . . . , Sac^(Num), where idx is the sample where SacPkVel_(i) occurs S30: SacAccProfMn^(DistrStat−HVR) DistrStat(·) over the mean of acceleration profiles of saccades: SacAccProfMn_(i) = Σ_(j=1) ^(N)|SacAcc_(i)(j)|/N, i = 1, . . . , Sac^(Num) S31: SacAccProfMd^(DistrStat−HVR) DistrStat(·) over the median of acceleration profiles of saccades: SacAccProfMd_(i) = median(|SacAcc_(i)(j)|), i = 1, . . . , Sac^(Num) S32: SacAccProfSd^(DistrStat−HVR) DistrStat(·) over the standard deviation of acceleration profiles of saccades: SacAccProfSd_(i) = {square root over (Σ_(j=1) ^(N)(|SacAcc_(i)(j)|−SacAccProfMn_(i))²/N)}, i = 1, . . . , Sac^(Num) S33: SacAccProfSk^(DistrStat−HVR) DistrStat(·) over the skewness of acceleration profiles of saccades: SacAccProfSk_(i) = $\frac{\sum_{j = 1}^{N}{\left( {{{{SacAcc}_{i}(j)}} - {SacAccProfMn}_{i}} \right)^{3}\text{/}N}}{{\sqrt{\sum_{j = 1}^{N}{\left( {{{{SacAcc}_{i}(j)}} - {SacAccProfMn}_{i}} \right)^{2}\text{/}N}}}^{3}},{i = 1},\ldots \mspace{14mu},{Sac}^{Num}$ S34: SacAccProfKu^(DistrStat−HVR) DistrStat(·) over the kurtosis of acceleration profiles of saccades: SacAccProfKu_(i) = $\frac{\sum_{j = 1}^{N}{\left( {{{{SacAcc}_{i}(j)}} - {SacAccProfMn}_{i}} \right)^{4}\text{/}N}}{\left( \sqrt{\sum_{j = 1}^{N}{\left( {{{{SacAcc}_{i}(j)}} - {SacAccProfMn}_{i}} \right)^{2}\text{/}N}} \right)^{2}},{i = 1},\ldots \mspace{14mu},{Sac}^{Num}$

Features of Saccade Characteristic Ratios

A very interesting and potentially informative category of features can be extracted by calculating the ratios of values from different pairs of features. These features represent the relative differences of feature values and thus they can be used to model various aspects of the multifaceted neural mechanisms guiding the saccades. Furthermore, the use of relative values instead of absolute values via the calculation of ratios can provide more robustness in cases when any effects from exogenous factors (like the exact stimulus layout) are not desirable.

TABLE XV Saccade ratio features S35: SacAmpDur_(Ratio) ^(DistrStat−HVR) DistrStat(·) over the amplitude-duration ratios of saccades: SacAmpDur_(Ratio) _(i) = ${{\frac{{SacAmp}_{i}}{{SacDur}_{i}}i} = 1},\ldots \mspace{14mu},{Sac}^{Num}$ S36: SacPkVelAmp_(Ratio) ^(DistrStat−HVR) DistrStat(·) over the peak velocity-amplitude ratios of saccades: SacPkVelAmp_(Ratio) _(i) = ${{\frac{{SacPkVel}_{i}}{{SacAmp}_{i}}i} = 1},\ldots \mspace{14mu},{sac}^{Num}$ S37: SacPkVelDur_(Ratio) ^(DistrStat−HVR) DistrStat(·) over the peak velocity-duration ratios of saccades: SacPkVelDur_(Ratio) _(i) = ${{\frac{{SacPkVel}_{i}}{{SacDur}_{i}}i} = 1},\ldots \mspace{14mu},{Sac}^{Num}$ S38: SacPkVelLocNoiseRatio^(DistrStat−R) DistrStat(·) over the peak velocity-local noise ratios of saccades: ${{SacPkVelLocNoise}_{{Ratio}_{i}} = {{\frac{{SacPkVel}_{i}}{{SacLocNoise}_{i}}i} = 1}}, \ldots \mspace{14mu}, {Sac}^{Num}, {{where}\mspace{14mu} {SacLocNoise}_{i}\mspace{14mu} {is}}$ calculated adaptively using the veolcity samples preceding a saccade S39: SacAccDecDur_(Ratio) ^(DistrStat) DistrStat(·) over the acceleration-deceleration duration ratios of saccades: ${{SacAccDecDur}_{{Ratio}_{i}} = {{\frac{{SacAcc}_{i}^{EndTime} - {SacAcc}_{i}^{StartTime}}{{SacDec}_{i}^{EndTime} - {SacAcc}_{i}^{StartTime}}i} = 1}}, \ldots \mspace{14mu}, {Sac}^{Num},{where}$ SacAcc_(i) ^(StartTime), SacAcc_(i) ^(EndTime), SacDec_(i) ^(StartTime), SacDec_(i) ^(EndTime) are the starting and ending times of the acceleration and decelerations phases of a saccade S40: SacPkAccPkDec_(Ratio) ^(DistrStat−HVR) DistrStat(·) over the peak acceleration-peak deceleration ratios of saccades: ${{SacPkAccPkDec}_{{Ratio}_{i}} = {{\frac{{SacPkAcc}_{i}}{{SacPkDec}_{i}}i} = 1}},\ldots \mspace{14mu},{Sac}^{Num}$

Features of Saccade Main Sequence

A more sophisticated approach to describe the relationships among the saccade characteristics is to model the behavior of all saccadic events collectively. In the past, such relationships have been explored for the case of amplitude-duration and peak velocity-amplitude, i.e. the so-called main sequence. FIG. 35 shows examples of saccade main sequence characteristics. In the case of amplitude-duration the relationship has been found to be linear whereas for the case of peak velocity-amplitude the relationship becomes non-linear for saccades of large amplitudes. For this reason, (saccade amplitudes during reading are relatively small)<8° a linear model may be employed to describe the relationship of the logarithms of peak velocity and amplitude.

TABLE XVI Saccade main sequence features S41: SacAmpDurFitLn_(Intercept) ^(R) The intercept from the linear-regression-fit performed collectively on all saccades to model the overall amplitude-duration relationship y = f(x), where y = SacAmp_(i), x = SacDur_(i), i = 1, . . . , Sac^(Num) S42: SacAmpDurFitLn_(Slope) ^(R) The slope from the linear-regression-fit performed collectively on all saccades to model the overall amplitude-duration relationship y = f(x), where y = SacAmp_(i), x = SacDur_(i), i = 1, . . . , Sac^(Num) S43: SacAmpDurFitLn_(R) ₂ ^(R) The R² from the linear-regression-fit performed collectively on all saccades to model the overall amplitude-duration relationship y = f(x), where y = SacAmp_(i), x = SacDur_(i), i = 1, . . . , Sac^(Num) S44: SacAmpDurRbFitLn_(Intercept) ^(R) The intercept from the robust linear-regression-fit performed collectively on all saccades to model the overall amplitude-duration relationship y = f(x), where y = SacAmp_(i), x = SacDur_(i), i = 1, . . . , Sac^(Num) S45: SacAmpDurRbFitLn_(Slope) ^(R) The slope from the robust linear-regression-fit performed collectively on all saccades to model the overall amplitude-duration relationship y = f(x), where y = SacAmp_(i), x = SacDur_(i), i = 1, . . . , Sac^(Num) S46: SacAmpDurRbFitLn_(R) ₂ ^(R) The R² from the robust linear-regression-fit performed collectively on all saccades to model the overall amplitude-duration relationship y = f(x), where y = SacAmp_(i), x = SacDur_(i), i = 1, . . . , Sac^(Num) S47: SacPkVelAmpFitLn_(Intercept) ^(R) The intercept from the linear-regression-fit performed collectively on all saccades to model the overall logarithm peak velocity-logarithm amplitude relationship y = f(x), where y = log(SacPkVel_(i)), x = log(SacAmp_(i)), i = 1, . . . , Sac^(Num) S48: SacPkVelAmpFitLn_(Slope) ^(R) The slope from the linear-regression-fit performed collectively on all saccades to model the overall logarithm peak velocity-logarithm amplitude relationship y = f(x), where y = log(SacPkVel_(i)), x = log(SacAmp_(i)), i = 1, . . . , Sac^(Num) S49: SacPkVelAmpFitLn_(R) ₂ ^(R) The R² from the linear-regression-fit performed collectively on all saccades to model the overall logarithm peak velocity-logarithm amplitude relationship y = f(x), where y = log(SacPkVel_(i)), x = log(SacAmp_(i)), i = 1, . . . , Sac^(Num) S50: SacPkVelAmpRbFitLn_(Intercept) ^(R) The intercept from the robust linear-regression-fit performed collectively on all saccades to model the overall logarithm peak velocity-logarithm amplitude relationship y = f(x), where y = log(SacPkVel_(i)), x = log(SacAmp_(i)), i = 1, . . . , Sac^(Num) S51: SacPkVelAmpRbFitLn_(Slope) ^(R) The slope from the robust linear-regression-fit performed collectively on all saccades to model the overall logarithm peak velocity-logarithm amplitude relationship y = f(x), where y = log(SacPkVel_(i)), x = log(SacAmp_(i)), i = 1, . . . , Sac^(Num) S52: SacPkVelAmpRbFitLn_(R) ₂ ^(R) The R² from the robust linear-regression-fit performed collectively on all saccades to model the overall logarithm peak velocity-logarithm amplitude relationship y = f(x), where y = log(SacPkVel_(i)), x = log(SacAmp_(i)), i = 1, . . . , Sac^(Num)

Glissade Features

A glissade is a small movement that may occasionally occur exactly after a saccade. Not all saccades are followed by a glissade, and additionally, the glissades are manifested in different forms, for example they can appear as a small rapid movement (dynamic overshoot) or as a slower and smoother movement (glissadic overshoot). The exact role of glissades during eye movement is not clear, and also, it has been found that their presence and appearance can be affected by the use of specific recording technologies and the application of filtering. Since these events appear frequently in eye tracking recordings it is valuable to extract features in order to model their characteristics.

Features of Glissade Temporal and Frequency-of-Occurrence Characteristics

This category contains features that are related to the general temporal characteristics of glissades and the frequency-of-appearance of their various types (manifestations). These features are important for quantifying the occurrence of glissades on specific subjects (idiosyncratic characteristics) but also for assessing the exact differences in glissade properties under various circumstances (e.g. the effects of fatigue).

TABLE XVII Glissade temporal features G01: GlsDur^(DistrStat) DistrStat(•) over the durations of glissades: GlsDur_(i), i = 1, . . . , Gls^(Num) G02: GlsInterv^(DistrStat) DistrStat(•) over the inter-glissade intervals: GlsInterv_(i) = Gls_(i) ^(StartTime) − Gls_(i−1) ^(EndTime), i = 2, . . . , Gls^(Num), where Gls_(i) ^(StartTime), Gls_(i−1) ^(EndTime) are the starting time of a glissade and the ending time of the previous glissade G03: Gls_(Pr) The percentage of saccades with a glissade: 100% · (Gls^(Num)/Sac^(Num)) G04: GlsSlow_(Pr) The percentage of slow glissades: 100% · (GlsSlow^(Num)/Gls^(Num)), where GlsSlow^(Num) is the number of slow glissades, i.e. 20°/s < peak glissade velocity < 45°/s G05: GlsModerate_(Pr) The percentage of moderate glissades 100% · (GlsModerate^(Num)/Gls^(Num)), where GlsModerate^(Num) is the number of moderate glissades, i.e. 45°/s < peak glissade velocity < 55°/s G06: GlsFast_(Pr) The percentage of fast glissades 100% · (GlsFast^(Num)/Gls^(Num)), where GlsFast^(Num) is the number of fast glissades, i.e. peak glissade velocity > 55°/s

Features of Glissade Shape

FIG. 36 shows examples of glissades and their basic characteristics. As it can be observed in FIG. 36, the glissades can be manifested with different ‘oscillatory’-like shapes. The feature extracted to describe such differences in glissade's ‘oscillatory’-like shape is calculated by measuring the number of local minima and maxima appearing in the position profiles of glissades.

TABLE XVIII Glissade shape features G07: GlsNumPosPksVlls^(DistrStat-HVR) DistrStat(•) over the number of local peaks and valleys in position signal of glissades: GlsNumLocPksVlls_(i) is the number of sign changes in vector SignPos(j) = sign(GlsPos_(i)(j) − GlsPos_(i)(j − 1)), j = 2, . . . , N, i = 1, . . . , Gls^(Num)

Features of Saccade-Adjacent Glissade Characteristic Ratios

Given the fact that a glissade occurs exactly after a saccade, a number of features can be extracted in order to model the relationships between the characteristics of a saccade and its adjacent glissade. These features can potentially reflect properties of the mechanisms that trigger a glissade after a specific saccade.

TABLE XIX Glissade-saccade ratio features G08: GlsSDurGDur_(Ratio) ^(DistrStat) DistrStat(·) over the saccade-adjacent glissade duration ratios: SDurGDur_(Ratio) _(i) = $\frac{{SacDur}_{i}}{{GlsDur}_{i}},{i = 1},\ldots \mspace{14mu},{Gls}^{Num}$ G09: GlsSAmpGDur_(Ratio) ^(DistrStat−HVR) DistrStat(·) over the saccade amplitudes-adjacent glissade duration ratios: ${{SAmpGDur}_{{Ratio}_{i}} = \frac{{SacAmp}_{i}}{{GlsDur}_{i}}},{i = 1},\ldots \mspace{14mu},{Gls}^{Num}$

Features of Glissade Velocity

In order to represent the velocity characteristics of glissades, their velocity profile shapes were modeled using the same descriptive statistics that have been used for the case of fixation and saccade profiles. FIG. SS7 shows an example of the velocity profile of a glissade.

TABLE XX Glissade velocity features G10: GlsVelProfMn^(DistrStat−HVR) DistrStat(·) over the mean of velocity profiles of glissades: GlsVelProfMn_(i) = Σ_(j=1) ^(N)|GlsVel_(i)(j)|/N, i = 1, . . . , Gls^(Num) G11: GlsVelProfMd^(DistrStat−HVR) DistrStat(·) over the median velocity profiles of glissades: GlsVelProfMd_(i) = median(|GlsVel_(i)|),i = 1, . . . , Gls^(Num) G12: GlsVelProfSd^(DistrStat−HVR) DistrStat(·) over the standard deviation of velocity profiles of glissades: GlsVelProfSd_(i) = {square root over (Σ_(j=1) ^(N)(|GlsVel_(i)(j)| − GlsVelProfMn_(i))²/N)}, i = 1, . . . , Gls^(Num) G13: GlsVelProfSk^(DistrStat−HVR) DistrStat(·) over the skewness of velocity profiles of glissades: GlsVelProfSk_(i) = $\frac{\sum_{j = 1}^{N}{\left( {{{{GlsVel}_{i}(j)}} - {GlsVelProfMn}_{i}} \right)^{3}\text{/}N}}{\left( \sqrt{\sum_{j = 1}^{N}{\left( {{{{GlsVel}_{i}(j)}} - {GlsVelProfMn}_{i}} \right)^{2}\text{/}N}} \right)^{3}},{i = 1},\ldots \mspace{14mu},{Gls}^{Num}$ G14: GlsVelProfKu^(DistrStat−HVR) DistrStat(·) over the kurtosis of velocity profiles of glissades: GlsVelProfKu_(i) = $\frac{\sum_{j = 1}^{N}{\left( {{{{GlsVel}_{i}(j)}} - {GlsVelProfMn}_{i}} \right)^{4}\text{/}N}}{\left( {\sum_{j = 1}^{N}{\left( {{{{GlsVel}_{i}(j)}} - {GlsVelProfMn}_{i}} \right)^{2}\text{/}N}} \right)^{2}},{i = 1},\ldots \mspace{14mu},{Gls}^{Num}$

Features of Glissade Acceleration

As previously, the shapes of the acceleration profiles of glissades were modeled by calculating descriptive statistics on their values. FIG. 37 shows examples of the horizontal and vertical components of the acceleration profile of a glissade.

TABLE XXI Glissade acceleration features G15: GlsAccProfMn^(DistrStat−HVR) DistrStat(·) over the mean of acceleration profiles of glissades: GlsAccProfMn_(i) = Σ_(j=1) ^(N)|GlsAcc_(i)(j)|/N, i = 1, . . . , Gls^(Num) G16: GlsAccProfMd^(DistrStat−HVR) DistrStat(·) over the median of acceleration profiles of glissades: GlsAccProfMd_(i) = median(|GlsAcc_(i)|), i = 1, . . . , Gls^(Num) G17: GlsAccProfSd^(DistrStat−HVR) DistrStat(·) over the standard deviation of acceleration profiles of glissades: GlsAccProfSd_(i) = {square root over (Σ_(j=1) ^(N)(|GlsAcc_(i)(j)| − GlsAccProfMn_(i))²/N)}, i = 1, . . . , Gls^(Num) G18: GlsAccProfSk^(DistrStat−HVR) DistrStat(·) over the skewness of acceleration profiles of glissades: GlsAccProfSk_(i) = $\frac{\sum_{j = 1}^{N}{\left( {{{{GlsAcc}_{i}(j)}} - {GlsAccProfMn}_{i}} \right)^{3}\text{/}N}}{\left( \sqrt{\sum_{j = 1}^{N}{\left( {{{{GlsAcc}_{i}(j)}} - {GlsAccProfMn}_{i}} \right)^{2}\text{/}N}} \right)^{3}},{i = 1},\ldots \mspace{14mu},{Gls}^{Num}$ G19: GlsAccProfKu^(DistrStat−HVR) DistrStat(·) over the kurtosis of acceleration profiles of glissades: GlsAccProfKu_(i) = $\frac{\sum_{j = 1}^{N}{\left( {{{{GlsAcc}_{i}(j)}} - {GlsAccProfMn}_{i}} \right)^{4}\text{/}N}}{\left( {\sum_{j = 1}^{N}{\left( {{{{GlsAcc}_{i}(j)}} - {GlsAccProfMn}_{i}} \right)^{2}\text{/}N}} \right)^{2}},{i = 1},\ldots \mspace{14mu},{Gls}^{Num}$

Reading Behavior Features

The features that are presented in this category are also extracted by fixations and saccades, however, we grouped them in this specialized section due to the fact that they can potentially represent some characteristics of the reading behavior of subjects, e.g. number of forward read words, number of returns to words (regressions/re-fixations), number of line changes etc. FIG. 38 shows eye movement signal and the corresponding pupil variation during a reading-pass of text stimulus. The examples show horizontal and vertical components of the eye movement during a reading-pass of the text stimulus.

TABLE XXII Reading behavior features R01: EndRead_(Pr) The percentage end-of-reading: 100% · (Read^(Dur)/Rec^(Dur)), where Read^(Dur) is the duration of the first complete reading pass of the presented text R02: ReadSpeedLnFit_(Slope) The slope of the linear-regression-fit performed collectively on all fixation centroids in vertical direction, during the first complete reading pass of the presented text visual stimulus R03: ReadSpeedLnFit_(R) ₂ The R² of the linear-regression-fit performed collectively on all fixation centroids in vertical direction, during the first complete reading pass of the presented text visual stimulus R04: SacSmRight_(Rate) The number of small rightward saccades per second, i.e. saccades where SacAmp_(i) ^(R) ≦ 8° and SacPos_(i) ^(H) (end) − SacPos_(i) ^(H) (start) > 0, i = 1, . . . , Sac^(Num) R05: SacSmLeft_(Rate) The number of small leftward saccades per second, i.e. saccades where SacAmp_(i) ^(R) ≦ 8° and SacPos_(i) ^(H) (end) − SacPos_(i) ^(H) < 0, i = 1, . . . , Sac^(Num) R06: SacLgRight_(Rate) The number of large rightward saccades per second, i.e. saccades where SacAmp_(i) ^(R) > 8° and SacPos_(i) ^(H) (end) − SacPos_(i) ^(H) (start) > 0, i = 1, . . . , Sac^(Num) R07: SacLgLeft_(Rate) The number of large leftward saccades per second, i.e. saccades where SacAmp_(i) ^(R) > 8° and SacPos_(i) ^(H) (end) − SacPos_(i) ^(H) (start) > 0, i = 1, . . . , Sac^(Num) R08: SacSmLeftSmRight_(Ratio) The ratio of the number of small leftward saccades to the number of small and rightward saccades (see above for definitions) R09: SacSmAllLgLeft_(Ratio) The ratio of the number of all small saccades to the number of large and leftward saccades (see above for definitions) R10: SacSmRightPosNegSlope_(Ratio) The ratio of the number of small rightward saccades with a positive slope to the number of small rightward saccades with a negative slope, where the slope is calculated via linear-regression-fit on SacPos_(i) ^(H), SacPos_(i) ^(V), i = 1, . . . , Sac^(Num) R11: SacSmLeftPosNegSlope_(Ratio) The ratio of the number of small leftward saccades with a positive slope to the small leftward saccades with a negative slope, where the slope is calculated via linear- regression-fit on SacPos_(i) ^(H), SacPos_(i) ^(V), i = 1, . . . , Sac^(Num) R12: SacSmPerLine^(DistrStat) The number of small saccades per line of text, where the line changes are identified as the points where large leftward saccades occur. The DistrStat here is applied on the lines of text and not on the event instances as in the other cases

Miscellaneous Features

In our study there is also a small group of complementary features that are not directly connected to fixations, saccades, or glissades. These features are related to signal invalidity and pupil size and they are potentially interesting for tasks like blink detection and pupilometry. In FIG. 8, some examples of the appearance of invalidity blocks and the pupil variation during a reading-pass may be observed.

TABLE XXIII Miscellaneous features M01: NanPrefilt_(Pr) The percentage of invalid (Nan) samples of the raw unfiltered recording: 100% · (NanPrefilt^(Num)/RecSamples^(Num)), where NanPrefilt^(Num) is the number of invalid samples and RecSamples^(Num) the total number of samples of the recording M02: NanPostfilt_(Pr) The percentage of invalid (Nan) samples in the filtered (e.g. using Savitzky-Golay smoothing filter) recording: 100% · (NanPostfilt^(Num)/RecSamples^(Num)), where NanPostfilt^(Num) is the total number of invalid samples after filtering M03: NanBlocks_(Rate) The rate of invalidity (Nan) blocks: NanBlocks^(Num)/Rec^(dur), where NanBlocks^(Num) is the number of invalidity blocks, i.e. blocks of consecutive invalid samples (after applying the filter) and Rec^(dur) the total recording duration (s) M04: PupilSize_(Md) The median pupil size calculated over raw pupil size samples (in arbitrary eye tracker units, invalid values excluded)

Experimental Results

Experiments were performed with the participation of 298 subjects (162 males-136 females) with ages from 18 to 46 years, (M=22, SD=4.3). All subjects had normal or corrected vision (147 normal/151 corrected with 61 glasses/86 contact lenses) and filled a questionnaire to verify that they did not have any recent severe head injury that could affect the oculomotor functionality. Apparatus and procedures were similar to those previously described herein.

Statistical Analysis of Features

In this section, tables are provided with the calculated statistics of central tendency (mean, M) and variance (standard deviation, SD) of the feature values over the experimental population of 298 subjects. Every table is split in two parts, the first part presents the statistics over the values of ‘non-distributional’ features and the second part presents the statistics over the values of ‘distributional’ features. For the case of the ‘distributional’ features it can be observed that the columns of the table are structured in two levels. Each of the main six columns corresponds to the feature values extracted by the application of descriptive statistics (MN, MD, SD, IQ, SK, KU) on all the instances of the basic feature-type (row) occurring in a recording. Each of these six columns is split in two, showing the corresponding means (M) and standard deviations (SD) over the subject population. For the features originally extracted for all components of movement (horizontal, vertical, and radial) the values corresponding only to the horizontal (H) and vertical (V) are presented, given the correlation of the radial component and the possibility to infer it from the two components of movement.

TABLE XXIV Normative values of fixation features over the experimental subject population. Non-Distribution Features Feature Type M SD F01 (s⁻¹) 3.80 0.43 F09 (%) H 9.59 3.45 V 9.79 3.28 F10 (%) H 7.69 2.64 V 8.68 2.75 F11 (%) H 7.52 2.47 V 8.85 2.70 F12 (%) H 75.19 6.39 V 72.68 6.26 F19 (°/s) 4.43 1.49 F20 (°/s) 14.65 4.59 F21 (°/s) 5.48 1.85

TABLE XXV Normative values of saccade features over the experimental subject population. Non-Distribution Features Feature Type M SD S02 (s⁻¹) 3.20 0.43 S27 (%) 3.75 4.50 S41 (°) −1.50 0.65 S42 (°/s) 0.14 0.03 S43 0.58 0.12 S44 (°) −1.27 0.69 S45 (°/s) 0.13 0.04 S46 0.58 0.12 S47 (°/s) 1.99 0.05 S48 (s⁻¹) 0.62 0.09 S49 0.86 0.07 S50 (°/s) 1.99 0.05 S51 (s⁻¹) 0.63 0.09 S52 0.86 0.07

TABLE XXVI Normative values of glissade features over the experimental subject population. Non-Distribution Features Feature Type M SD G03 (%) 61.11 22.12 G04 (%) 70.79 20.97 G05 (%) 11.32 5.76 G06 (%) 17.89 18.81

TABLE XXVII Normative values of reading behavior features over the experimental subject population. Non-Distribution Features Feature Type M SD R01 (%) 83.83 14.70 R02 (°/s) 0.59 0.15 R03 0.97 0.04 R04 (s⁻¹) 2.39 0.38 R05 (s⁻¹) 0.80 0.34 R06 (s⁻¹) 0.03 0.03 R07 (s⁻¹) 0.41 0.12 R08 0.35 0.17 R09 8.49 2.83 R10 1.35 1.03 R11 1.58 1.63 Distribution Features Descriptive Statistic: DistrStat(•) Feature MN MD SD IQ SK KU Type M SD M SD M SD M SD M SD M SD R12 (s) 8.03 2.42 7.03 2.11 4.27 2.36 4.53 2.61 1.12 0.72 4.50 2.33

TABLE XXVIII Normative values of miscellaneous features over the experimental subject population. Non-Distribution Features Feature Type M SD M01 (%) 2.91 3.44 M02 (%) 6.05 5.48 M03 (s⁻¹) 0.23 0.16 M04 (a.u.) 2372.18 782.10

The presented feature set is composed of a total of 115 different types of eye movement features. There are 28 features extracted from fixations, 52 extracted from saccades, 19 extracted from glissades, 12 specialized reading-behavior features, and 4 miscellaneous features. The ‘distributional’ feature-types contribute x6 feature values to the total set (due to the 6 statistics), whereas the ‘non-distributional’ feature-types contribute x1 feature values. In this example, 63.5% of the feature-types are ‘distributional’ and 36.5% are ‘non-distributional’. From these, some are extracted only from the radial component or the 2D-trajectory or as unique values and contribute x1 feature values, some are extracted from the horizontal and vertical components and contribute x2 feature values, and some from all components and contribute x3 values. This leads to a final set that contains 1029 unique feature values.

Many features of the created large feature set are expected to be highly correlated. For example, in the case of the descriptive statistics used to for the ‘distributional’ features two measures of central tendency (mean, median) and two measures of variance (standard deviation, inter-quartile range) were evaluated. The inclusion of those features allows selection the most suitable measure either by considering qualitative characteristics of the application under investigation or by performing a quantitative evaluation of the reliability of features via test-retest measures of absolute agreement and consistency.

The inspection of the values of the extracted features in Tables XXIV to XXVIII can provide valuable evidence about the normative oculomotor behavior during the task of reading. The extracted features and their normative values can be of interest. For example, the temporal characteristics of fixations have been reported to be influenced by cognitive fatigue and by medical conditions that can affect normal cognitive functionality, e.g. the onset of Alzheimer disease. The values of fixation drift can provide important cues both during the assessment of eye movements in clinical studies, and in studies related to human-computer interaction. Furthermore, features of fixation velocity and velocity noise can be valuable for modeling subject-specific individualities and abnormalities caused by medical conditions like multiple sclerosis and Alzheimer's disease.

The saccade features can also provide important information for various applications. The duration of saccades has been explored in the past in the field of human computer interaction, and also, the existence of slow-saccades has been reported in cases of clinical disorders. An increase rate of saccades was observed in the case of children with autism when compared to normal controls. The curvature of saccade trajectory can reflect the distractor-related modulation of eye movements. Furthermore, the dynamic characteristics of saccades and their relationships (e.g. the main sequence) have been investigated in the field of ergonomics as a gauge of mental load. The main sequence characteristics, along with the acceleration of saccades have been investigated in the field of biometrics as well. Also, the relative characteristics of the saccade acceleration-deceleration phases have been found to present differentiations in the autism spectrum disorder. Glissades may provide information about subject-specific traits with use in biometric and medical applications. Finally, the inspection of normative reading behavior values may provide useful information during the assessment of medical conditions, e.g. in the case of Alzheimer disease.

Additional Experimental Results

FIG. 39 shows results from application of the technology from 17 Subjects with mTBI (mild traumatic brain injury) versus 298 Control Subjects. A comparison of 17 subjects with mTBI compared to 298 control subjects was conducted. Eye-movement signal was examined from two tasks: (1) Poetry Reading and (2) Random Saccades. By running the technology described in this document following results were achieved. For Poetry Reading 13 eye-movement factors were extracted, 3 of which were significantly different for mTBI versus control (FIG. 39, graphs a, b and c). Patients with mTBI had reduced vertical saccade speed, reduced saccade rate and reduced saccade curvature than controls. For the random saccade task, patients with mTBI had reduced vertical saccade velocity variability (FIG. 39, graph d). All of the effect sizes for the findings (0.61, 0.63. 0.65, 0.66, mean=0.64) are in the same range, if slightly larger than effect sizes for eye movement findings in the literature (0.58).

In various embodiments, methods as described herein include the extraction of eye movement features during the task of reading and presented normative values computed from eye tracking recordings captured from a large population of subjects. Normative values can provide data that can be used during the assessment of abnormalities of the oculomotor system. Also, the tables allow for a comparative inspection of the values of correlated features and thus they can facilitate feature selection.

Further modifications and alternative embodiments of various aspects of the invention may be apparent to those skilled in the art in view of this description. Accordingly, this description is to be construed as illustrative only and is for the purpose of teaching those skilled in the art the general manner of carrying out the invention. It is to be understood that the forms of the invention shown and described herein are to be taken as embodiments. Elements and materials may be substituted for those illustrated and described herein, parts and processes may be reversed, and certain features of the invention may be utilized independently, all as would be apparent to one skilled in the art after having the benefit of this description of the invention. Methods may be implemented manually, in software, in hardware, or a combination thereof. The order of any method may be changed, and various elements may be added, reordered, combined, omitted, modified, etc. Changes may be made in the elements described herein without departing from the spirit and scope of the invention as described in the following claims. 

1. A method of assessing brain injuries and other health states, comprising: measuring, by one or more sensors, eye movement of a person; determining, by a biometric assessment system implemented on one or more computing devices, one or more values based at least in part on at least a portion of the measured eye movement; and assessing, based at least in part on at least one of the determined one or more values, whether or not the person has suffered brain injury.
 2. (canceled)
 3. The method of assessing brain injuries and other health states of claim 1, wherein determining one or more values comprises determining one or more feature vectors associated with eye movement of the person.
 4. (canceled)
 5. The method of assessing brain injuries and other health states of claim 1, wherein measuring eye movement of a person comprises detecting one or more fixations.
 6. The method of assessing brain injuries and other health states of claim 1, wherein measuring eye movement of a person comprises detecting one or more saccades.
 7. The method of assessing brain injuries of claim 1, wherein measuring eye movement of a person comprises detecting one or more glissades.
 8. The method of assessing brain injuries and other health states of claim 1, wherein at least one of the values comprises a fixation quantitative score.
 9. The method of assessing brain injuries and other health states of claim 1, wherein at least one of the values comprises a fixation count.
 10. The method of assessing brain injuries and other health states of claim 1, wherein at least one of the values comprises a fixation duration.
 11. The method of assessing brain injuries and other health states of claim 1, wherein at least one of the values comprises a vectorial saccade amplitude.
 12. The method of assessing brain injuries and other health states of claim 1, wherein at least one of the values comprises an overshoot.
 13. The method of assessing brain injuries and other health states of claim 1, wherein at least one of the values comprises an undershoot.
 14. The method of assessing brain injuries and other health states of claim 1, wherein at least one of the values comprises agonist muscle activation.
 15. The method of assessing brain injuries and other health states of claim 1, wherein at least one values is based on measurements of one or more characteristics associated with conscious behavior.
 16. The method of assessing brain injuries and other health states of claim 1, wherein at least one values is based on measurements of one or more characteristics associated with subconscious behavior.
 17. (canceled)
 18. The method of assessing brain injuries and other health states of claim 1, wherein assessing whether or not the person has suffered brain injury comprises cross-validating two or more values associated with the measured eye movements.
 19. The method of assessing brain injuries and other health states of claim 1, wherein assessing whether or not the person has suffered brain injury comprises detecting one or more patterns in the person's eye movements.
 20. The method of assessing brain injuries and other health states of claim 1, wherein assessing whether or not the person has suffered brain injury comprises detecting one or more changes in the person's ocular behavior over time. 21-24. (canceled)
 25. The method of assessing brain injuries and other health states of claim 1, further comprising estimating, by the biometric assessment system, at least one of the anatomical characteristics of an oculomotor plant of the person, wherein at least one of the values is based at least in part on the estimates of at least one of the anatomical characteristics.
 26. The method of assessing brain injuries and other health states of claim 25, wherein estimating at least one of the anatomical characteristics of an oculomotor plant of the person comprises creating a two-dimensional mathematical model including at least one of the anatomical characteristics of the oculomotor plant.
 27. The method of assessing brain injuries and other health states of claim 1, further comprising estimating one or more properties of the complex eye movement patterns based at least in part on at least a portion of the measured eye movement, wherein at least one of the values is based at least in part on the estimates of at least one of the properties of the complex eye movement patterns.
 28. The method of assessing brain injuries and other health states of claim 1, further comprising generating one or more brain's control strategies in guiding visual attention from eye movements of the person in a form of complex eye movement patterns, wherein the assessment of whether or not the person has suffered brain injury is based in part on one or more components of complex eye movement patterns.
 29. The method of assessing brain injuries and other health states of claim 1, further comprising measuring one or more characteristics of an iris or a periocular region of the eye of the person.
 30. (canceled)
 31. The method of assessing brain injuries and other health states of claim 1, wherein assessing whether or not the person has suffered brain injury comprises generating, by the biometric assessment system, one or more fixation density maps.
 32. The method of assessing brain injuries and other health states of claim 1, wherein assessing whether or not the person has suffered brain injury comprises assessing micro eye movement.
 33. The method of assessing brain injuries and other health states of claim 1, wherein assessing whether or not the person has suffered brain injury comprises assessing complex oculomotor behavior.
 34. (canceled)
 35. A system, comprising: an instrument configured to measure eye movement of a person; and a biometric assessment system implemented on one or more computing devices and coupled to the instrument, the biometric assessment system being configured to: determine one or more values based at least in part on eye movement measured by the instrument; and assess, based at least in part on at least one of the determined one or more values, whether or not the person has suffered brain injury. 36-37. (canceled)
 38. A non-transitory, computer-readable storage medium comprising program instructions stored thereon, wherein the program instructions, when executed on one or more computers, cause the one or more computers to implement a biometric assessment system configured to: measure eye movement of a person; determine one or more values based at least in part on at least a portion of the measured eye movement; and assess, based at least in part on at least one of the determined one or more values, whether or not the person has suffered brain injury. 39-85. (canceled) 